Relevant bibliographies by topics / Surrogate methods

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  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Surrogate methods'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Surrogate methods"

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Ciani, Oriana, Sarah Davis, Paul Tappenden, Ruth Garside, Ken Stein, Anna Cantrell, Everardo D. Saad, Marc Buyse, and Rod S. Taylor. "VALIDATION OF SURROGATE ENDPOINTS IN ADVANCED SOLID TUMORS: SYSTEMATIC REVIEW OF STATISTICAL METHODS, RESULTS, AND IMPLICATIONS FOR POLICY MAKERS." International Journal of Technology Assessment in Health Care 30, no. 3 (July 2014): 312–24. http://dx.doi.org/10.1017/s0266462314000300.

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Objectives: Licensing of, and coverage decisions on, new therapies should rely on evidence from patient-relevant endpoints such as overall survival (OS). Nevertheless, evidence from surrogate endpoints may also be useful, as it may not only expedite the regulatory approval of new therapies but also inform coverage decisions. It is, therefore, essential that candidate surrogate endpoints be properly validated. However, there is no consensus on statistical methods for such validation and on how the evidence thus derived should be applied by policy makers.Methods: We review current statistical approaches to surrogate-endpoint validation based on meta-analysis in various advanced-tumor settings. We assessed the suitability of two surrogates (progression-free survival [PFS] and time-to-progression [TTP]) using three current validation frameworks: Elston and Taylor's framework, the German Institute of Quality and Efficiency in Health Care's (IQWiG) framework and the Biomarker-Surrogacy Evaluation Schema (BSES3).Results: A wide variety of statistical methods have been used to assess surrogacy. The strength of the association between the two surrogates and OS was generally low. The level of evidence (observation-level versus treatment-level) available varied considerably by cancer type, by evaluation tools and was not always consistent even within one specific cancer type.Conclusions: Not in all solid tumors the treatment-level association between PFS or TTP and OS has been investigated. According to IQWiG's framework, only PFS achieved acceptable evidence of surrogacy in metastatic colorectal and ovarian cancer treated with cytotoxic agents. Our study emphasizes the challenges of surrogate-endpoint validation and the importance of building consensus on the development of evaluation frameworks.
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Rios, Ricardo Araújo, Michael Small, and Rodrigo Fernandes de Mello. "Testing for Linear and Nonlinear Gaussian Processes in Nonstationary Time Series." International Journal of Bifurcation and Chaos 25, no. 01 (January 2015): 1550013. http://dx.doi.org/10.1142/s0218127415500133.

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Surrogate data methods have been widely applied to produce synthetic data, while maintaining the same statistical properties as the original. By using such methods, one can analyze certain properties of time series. In this context, Theiler's surrogate data methods are the most commonly considered approaches. These are based on the Fourier transform, limiting them to be applied only on stationary time series. Consequently, time series including nonstationary behavior, such as trend, produces spurious high frequencies with Theiler's methods, resulting in inconsistent surrogates. To solve this problem, we present two new methods that combine time series decomposition techniques and surrogate data methods. These new methods initially decompose time series into a set of monocomponents and the trend. Afterwards, traditional surrogate methods are applied on those individual monocomponents and a set of surrogates is obtained. Finally, all individual surrogates plus the trend signal are combined in order to create a single surrogate series. Using this method, one can investigate linear and nonlinear Gaussian processes in time series, irrespective of the presence of nonstationary behavior.
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Hernandez-Villafuerte, Karla, Alastair Fischer, and Nicholas Latimer. "CHALLENGES AND METHODOLOGIES IN USING PROGRESSION FREE SURVIVAL AS A SURROGATE FOR OVERALL SURVIVAL IN ONCOLOGY." International Journal of Technology Assessment in Health Care 34, no. 3 (2018): 300–316. http://dx.doi.org/10.1017/s0266462318000338.

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Objectives:A primary outcome in oncology trials is overall survival (OS). However, to estimate OS accurately requires a sufficient number of patients to have died, which may take a long time. If an alternative end point is sufficiently highly correlated with OS, it can be used as a surrogate. Progression-free survival (PFS) is the surrogate most often used in oncology, but does not always satisfy the correlation conditions for surrogacy. We analyze the methodologies used when extrapolating from PFS to OS.Methods:Davis et al. previously reviewed the use of surrogate end points in oncology, using papers published between 2001 and 2011. We extend this, reviewing papers published between 2012 and 2016. We also examine the reporting of statistical methods to assess the strength of surrogacy.Results:The findings from 2012 to 2016 do not differ substantially from those of 2001 to 2011: the same factors are shown to affect the relationship between PFS and OS. The proportion of papers reporting individual patient data (IPD), strongly recommended for full assessment of surrogacy, remains low: 33 percent. A wide range of methods has been used to determine the appropriateness of surrogates. While usually adhering to reporting standards, the standard of scholarship appears sometimes to be questionable and the reporting of results often haphazard.Conclusions:Standards of analysis and reporting PFS to OS surrogate studies should be improved by increasing the rigor of statistical reporting and by agreeing to a minimum set of reporting guidelines. Moreover, the use of IPD to assess surrogacy should increase.
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Lu, Dan, and Daniel Ricciuto. "Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques." Geoscientific Model Development 12, no. 5 (May 6, 2019): 1791–807. http://dx.doi.org/10.5194/gmd-12-1791-2019.

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Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine-learning-based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.
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Ciani, Oriana, Bogdan Grigore, Hedwig Blommestein, Saskia de Groot, Meilin Möllenkamp, Stefan Rabbe, Rita Daubner-Bendes, and Rod S. Taylor. "Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies." Medical Decision Making 41, no. 4 (March 10, 2021): 439–52. http://dx.doi.org/10.1177/0272989x21994553.

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Background Surrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology assessment (HTA). Objectives 1) To map methodologies for the validation of surrogate endpoints and 2) to determine their impact on acceptability of surrogates and coverage decisions made by HTA agencies. Methods We sought HTA reports where evaluation relied on a surrogate from 8 HTA agencies. We extracted data on the methods applied for surrogate validation. We assessed the level of agreement between agencies and fitted mixed-effects logistic regression models to test the impact of validation approaches on the agency’s acceptability of the surrogate endpoint and their coverage recommendation. Results Of the 124 included reports, 61 (49%) discussed the level of evidence to support the relationship between the surrogate and the patient-centered endpoint, 27 (22%) reported a correlation coefficient/association measure, and 40 (32%) quantified the expected effect on the patient-centered outcome. Overall, the surrogate endpoint was deemed acceptable in 49 (40%) reports ( k-coefficient 0.10, P = 0.004). Any consideration of the level of evidence was associated with accepting the surrogate endpoint as valid (odds ratio [OR], 4.60; 95% confidence interval [CI], 1.60–13.18, P = 0.005). However, we did not find strong evidence of an association between accepting the surrogate endpoint and agency coverage recommendation (OR, 0.71; 95% CI, 0.23–2.20; P = 0.55). Conclusions Handling of surrogate endpoint evidence in reports varied greatly across HTA agencies, with inconsistent consideration of the level of evidence and statistical validation. Our findings call for careful reconsideration of the issue of surrogacy and the need for harmonization of practices across international HTA agencies.
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Scher, Howard I., Glenn Heller, Arturo Molina, Gerhardt Attard, Daniel C. Danila, Xiaoyu Jia, Weimin Peng, et al. "Circulating Tumor Cell Biomarker Panel As an Individual-Level Surrogate for Survival in Metastatic Castration-Resistant Prostate Cancer." Journal of Clinical Oncology 33, no. 12 (April 20, 2015): 1348–55. http://dx.doi.org/10.1200/jco.2014.55.3487.

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Purpose Trials in castration-resistant prostate cancer (CRPC) need new clinical end points that are valid surrogates for survival. We evaluated circulating tumor cell (CTC) enumeration as a surrogate outcome measure. Patients and Methods Examining CTCs alone and in combination with other biomarkers as a surrogate for overall survival was a secondary objective of COU-AA-301, a multinational, randomized, double-blind phase III trial of abiraterone acetate plus prednisone versus prednisone alone in patients with metastatic CRPC previously treated with docetaxel. The biomarkers were measured at baseline and 4, 8, and 12 weeks, with 12 weeks being the primary measure of interest. The Prentice criteria were applied to test candidate biomarkers as surrogates for overall survival at the individual-patient level. Results A biomarker panel using CTC count and lactate dehydrogenase (LDH) level was shown to satisfy the four Prentice criteria for individual-level surrogacy. Twelve-week surrogate biomarker data were available for 711 patients. The abiraterone acetate plus prednisone and prednisone-alone groups demonstrated a significant survival difference (P = .034); surrogate distribution at 12 weeks differed by treatment (P < .001); the discriminatory power of the surrogate to predict mortality was high (weighted c-index, 0.81); and adding the surrogate to the model eliminated the treatment effect on survival. Overall, 2-year survival of patients with CTCs < 5 (low risk) versus patients with CTCs ≥ 5 cells/7.5 mL of blood and LDH > 250 U/L (high risk) at 12 weeks was 46% and 2%, respectively. Conclusion A biomarker panel containing CTC number and LDH level was shown to be a surrogate for survival at the individual-patient level in this trial of abiraterone acetate plus prednisone versus prednisone alone for patients with metastatic CRPC. Additional trials are ongoing to validate the findings.
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Hu, Zhen, Saideep Nannapaneni, and Sankaran Mahadevan. "Efficient Kriging surrogate modeling approach for system reliability analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, no. 2 (May 2017): 143–60. http://dx.doi.org/10.1017/s089006041700004x.

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AbstractCurrent limit state surrogate modeling methods for system reliability analysis usually build surrogate models for failure modes individually or build composite limit states. In practical engineering applications, multiple system responses may be obtained from a single setting of inputs. In such cases, building surrogate models individually will ignore the correlation between different system responses and building composite limit states may be computationally expensive because the nonlinearity of composite limit state is usually higher than individual limit states. This paper proposes a new efficient Kriging surrogate modeling approach for system reliability analysis by constructing composite Kriging surrogates through selection of Kriging surrogates constructed individually and Kriging surrogates built based on singular value decomposition. The resulting composite surrogate model will combine the advantages of both types of Kriging surrogate models and thus reduce the number of required training points. A new stopping criterion and a new surrogate model refinement strategy are proposed to further improve the efficiency of this approach. The surrogate models are refined adaptively with high accuracy near the active failure boundary until the proposed new stopping criterion is satisfied. Three numerical examples including a series, a parallel, and a combined system are used to demonstrate the effectiveness of the proposed method.
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Oko, S. O. "Surrogate methods for linear inequalities." Journal of Optimization Theory and Applications 72, no. 2 (February 1992): 247–68. http://dx.doi.org/10.1007/bf00940518.

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Kim, Hyejin, Janet A. Deatrick, and Connie M. Ulrich. "Ethical frameworks for surrogates’ end-of-life planning experiences." Nursing Ethics 24, no. 1 (August 3, 2016): 46–69. http://dx.doi.org/10.1177/0969733016638145.

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Background: Despite the growing body of knowledge about surrogate decision making, we know very little about the use of ethical frameworks (including ethical theories, principles, and concepts) to understand surrogates’ day-to-day experiences in end-of-life care planning for incapacitated adults. Objectives and Methods: This qualitative systematic review was conducted to identify the types of ethical frameworks used to address surrogates’ experiences in end-of-life care planning for incapacitated adults as well as the most common themes or patterns found in surrogate decision-making research. Findings: Seven research papers explicitly identified ethical theories, principles, or concepts, such as autonomy, substituted judgment, and best interest standards as guidelines for the research. Surrogate decision making themes included the responsibilities and goals of being a surrogate, factors influencing surrogates’ decision making, outcomes for surrogates, and an overarching theme of “wanting to do the right thing” for their loved one and/or themselves. Discussion: Understanding the complexity of surrogates’ experiences of end-of-life care planning is beyond the scope of conventional ethical frameworks. Conclusion: Ethical frameworks that address individuality and contextual variations related to decision making may more appropriately guide surrogate decision-making research that explores surrogates’ end-of-life care planning experiences.
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Royce, Trevor Joseph, Ming-Hui Chen, Jing Wu, Marian Loffredo, Andrew A. Renshaw, Philip W. Kantoff, and Anthony Victor D'Amico. "A comparison of surrogate endpoints for all cause mortality in men with localized unfavorable-risk prostate cancer." Journal of Clinical Oncology 35, no. 6_suppl (February 20, 2017): 21. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.21.

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21 Background: Several surrogates for prostate cancer-specific mortality exist, but whether these are surrogates for all cause mortality (ACM), and how their performance compares, is unknown. We investigated the relative efficacy of 4 candidate surrogates for ACM using the proportion of treatment effect (PTE) metric. Methods: Two-hundred and six men with localized unfavorable-risk prostate cancer were randomized to radiation therapy (RT) or RT and 6 months of androgen-deprivation therapy (ADT) from 1995 to 2001 and followed for a median of 16.62 years. Among the 159 men with no or minimal comorbidity, a significant reduction in the risk of death was observed in those randomized to RT and ADT versus RT alone; these 159 men formed the study cohort. In order to assess whether the candidate surrogated satisfied Prentice’s criteria for surrogacy, Cox regression analyses were performed to assess the risk of death for each of the candidate surrogates and treatment before and after adjusting for prostate-specific antigen (PSA), age at randomization, T category, and Gleason score. Results: PSA nadir > 0.5 ng/mL, PSA doubling time < 9 months and interval to PSA failure < 30 months met Prentice’s criteria for surrogacy (P = 0.01, 0.003, and 0.03 for the surrogate covariate in the multivariable model, respectively) for ACM, while PSA failure did not (P = 0.10). For the three surrogates, the PTE values were 103.86%, 43.09%, and 41.26%, respectively. Conclusions: A PSA nadir value of > 0.5 ng/mL following RT and ADT identified men prior to PSA failure who were at high-risk for death and therefore could be used to select men for entry, at the time of PSA nadir and before PSA failure, onto randomized trials evaluating the impact on survival of salvage ADT with or without agents shown to prolong survival in men with castrate-resistant metastatic prostate cancer. By enriching study cohorts with men who have achieved a surrogate endpoint for ACM, one can enhance the likelihood that the study will be able to observe whether survival is prolonged when novel treatment is added to ADT, as compared to ADT alone, in an abbreviated time period. Clinical Trial Number: NCT00116220
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Dissertations / Theses on the topic "Surrogate methods"

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Conradie, Tanja. "Modelling of nonlinear dynamic systems : using surrogate data methods." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51834.

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Thesis (MSc)--Stellenbosch University, 2000.
ENGLISH ABSTRACT: This study examined nonlinear modelling techniques as applied to dynamic systems, paying specific attention to the Method of Surrogate Data and its possibilities. Within the field of nonlinear modelling, we examined the following areas of study: attractor reconstruction, general model building techniques, cost functions, description length, and a specific modelling methodology. The Method of Surrogate Data was initially applied in a more conventional application, i.e. testing a time series for nonlinear, dynamic structure. Thereafter, it was used in a less conventional application; i.e. testing the residual vectors of a nonlinear model for membership of identically and independently distributed (i.i.d) noise. The importance of the initial surrogate analysis of a time series (determining whether the apparent structure of the time series is due to nonlinear, possibly chaotic behaviour) was illustrated. This study confrrmed that omitting this crucial step could lead to a flawed conclusion. If evidence of nonlinear structure in the time series was identified, a radial basis model was constructed, using sophisticated software based on a specific modelling methodology. The model is an iterative algorithm using minimum description length as the stop criterion. The residual vectors of the models generated by the algorithm, were tested for membership of the dynamic class described as i.i.d noise. The results of this surrogate analysis illustrated that, as the model captures more of the underlying dynamics of the system (description length decreases), the residual vector resembles Li.d noise. It also verified that the minimum description length criterion leads to models that capture the underlying dynamics of the time series, with the residual vector resembling Li.d noise. In the case of the "worst" model (largest description length), the residual vector could be distinguished from Li.d noise, confirming that it is not the "best" model. The residual vector of the "best" model (smallest description length), resembled Li.d noise, confirming that the minimum description length criterion selects a model that captures the underlying dynamics of the time series. These applications were illustrated through analysis and modelling of three time series: a time series generated by the Lorenz equations, a time series generated by electroencephalograhpic signal (EEG), and a series representing the percentage change in the daily closing price of the S&P500 index.
AFRIKAANSE OPSOMMING: In hierdie studie ondersoek ons nie-lineere modelleringstegnieke soos toegepas op dinamiese sisteme. Spesifieke aandag word geskenk aan die Metode van Surrogaat Data en die moontlikhede van hierdie metode. Binne die veld van nie-lineere modellering het ons die volgende terreine ondersoek: attraktor rekonstruksie, algemene modelleringstegnieke, kostefunksies, beskrywingslengte, en 'n spesifieke modelleringsalgoritme. Die Metode and Surrogaat Data is eerstens vir 'n meer algemene toepassing gebruik wat die gekose tydsreeks vir aanduidings van nie-lineere, dimanise struktuur toets. Tweedens, is dit vir 'n minder algemene toepassing gebruik wat die residuvektore van 'n nie-lineere model toets vir lidmaatskap van identiese en onafhanlike verspreide geraas. Die studie illustreer die noodsaaklikheid van die aanvanklike surrogaat analise van 'n tydsreeks, wat bepaal of die struktuur van die tydsreeks toegeskryf kan word aan nie-lineere, dalk chaotiese gedrag. Ons bevesting dat die weglating van hierdie analise tot foutiewelike resultate kan lei. Indien bewyse van nie-lineere gedrag in die tydsreeks gevind is, is 'n model van radiale basisfunksies gebou, deur gebruik te maak van gesofistikeerde programmatuur gebaseer op 'n spesifieke modelleringsmetodologie. Dit is 'n iteratiewe algoritme wat minimum beskrywingslengte as die termineringsmaatstaf gebruik. Die model se residuvektore is getoets vir lidmaatskap van die dinamiese klas wat as identiese en onafhanlike verspreide geraas bekend staan. Die studie verifieer dat die minimum beskrywingslengte as termineringsmaatstaf weI aanleiding tot modelle wat die onderliggende dinamika van die tydsreeks vasvang, met die ooreenstemmende residuvektor wat nie onderskei kan word van indentiese en onafhanklike verspreide geraas nie. In die geval van die "swakste" model (grootse beskrywingslengte), het die surrogaat analise gefaal omrede die residuvektor van indentiese en onafhanklike verspreide geraas onderskei kon word. Die residuvektor van die "beste" model (kleinste beskrywingslengte), kon nie van indentiese en onafhanklike verspreide geraas onderskei word nie en bevestig ons aanname. Hierdie toepassings is aan die hand van drie tydsreekse geillustreer: 'n tydsreeks wat deur die Lorenz vergelykings gegenereer is, 'n tydsreeks wat 'n elektroenkefalogram voorstel en derdens, 'n tydsreeks wat die persentasie verandering van die S&P500 indeks se daaglikse sluitingsprys voorstel.
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Asritha, Kotha Sri Lakshmi Kamakshi. "Comparing Random forest and Kriging Methods for Surrogate Modeling." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20230.

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The issue with conducting real experiments in design engineering is the cost factor to find an optimal design that fulfills all design requirements and constraints. An alternate method of a real experiment that is performed by engineers is computer-aided design modeling and computer-simulated experiments. These simulations are conducted to understand functional behavior and to predict possible failure modes in design concepts. However, these simulations may take minutes, hours, days to finish. In order to reduce the time consumption and simulations required for design space exploration, surrogate modeling is used. \par Replacing the original system is the motive of surrogate modeling by finding an approximation function of simulations that is quickly computed. The process of surrogate model generation includes sample selection, model generation, and model evaluation. Using surrogate models in design engineering can help reduce design cycle times and cost by enabling rapid analysis of alternative designs.\par Selecting a suitable surrogate modeling method for a given function with specific requirements is possible by comparing different surrogate modeling methods. These methods can be compared using different application problems and evaluation metrics. In this thesis, we are comparing the random forest model and kriging model based on prediction accuracy. The comparison is performed using mathematical test functions. This thesis conducted quantitative experiments to investigate the performance of methods. After experimental analysis, it is found that the kriging models have higher accuracy compared to random forests. Furthermore, the random forest models have less execution time compared to kriging for studied mathematical test problems.
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Kamath, Atul Krishna. "Surrogate-assisted optimisation-based verification & validation." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15637.

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This thesis deals with the application of optimisation based Validation and Verification (V&V) analysis on aerospace vehicles in order to determine their worst case performance metrics. To this end, three aerospace models relating to satellite and launcher vehicles provided by European Space Agency (ESA) on various projects are utilised. As a means to quicken the process of optimisation based V&V analysis, surrogate models are developed using polynomial chaos method. Surro- gate models provide a quick way to ascertain the worst case directions as computation time required for evaluating them is very small. A sin- gle evaluation of a surrogate model takes less than a second. Another contribution of this thesis is the evaluation of operational safety margin metric with the help of surrogate models. Operational safety margin is a metric defined in the uncertain parameter space and is related to the distance between the nominal parameter value and the first instance of performance criteria violation. This metric can help to gauge the robustness of the controller but requires the evaluation of the model in the constraint function and hence could be computationally intensive. As surrogate models are computationally very cheap, they are utilised to rapidly compute the operational safety margin metric. But this metric focuses only on finding a safe region around the nominal parameter value and the possibility of other disjoint safe regions are not explored. In order to find other safe or failure regions in the param- eter space, the method of Bernstein expansion method is utilised on surrogate polynomial models to help characterise the uncertain param- eter space into safe and failure regions. Furthermore, Binomial failure analysis is used to assign failure probabilities to failure regions which might help the designer to determine if a re-design of the controller is required or not. The methodologies of optimisation based V&V, surrogate modelling, operational safety margin, Bernstein expansion method and risk assessment have been combined together to form the WCAT-II MATLAB toolbox.
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Heap, Ryan C. "Real-Time Visualization of Finite Element Models Using Surrogate Modeling Methods." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/6536.

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Finite element analysis (FEA) software is used to obtain linear and non-linear solutions to one, two, and three-dimensional (3-D) geometric problems that will see a particular load and constraint case when put into service. Parametric FEA models are commonly used in iterative design processes in order to obtain an optimum model given a set of loads, constraints, objectives, and design parameters to vary. In some instances it is desirable for a designer to obtain some intuition about how changes in design parameters can affect the FEA solution of interest, before simply sending the model through the optimization loop. This could be accomplished by running the FEA on the parametric model for a set of part family members, but this can be very timeconsuming and only gives snapshots of the models real behavior. The purpose of this thesis is to investigate a method of visualizing the FEA solution of the parametric model as design parameters are changed in real-time by approximating the FEA solution using surrogate modeling methods. The tools this research will utilize are parametric FEA modeling, surrogate modeling methods, and visualization methods. A parametric FEA model can be developed that includes mesh morphing algorithms that allow the mesh to change parametrically along with the model geometry. This allows the surrogate models assigned to each individual node to use the nodal solution of multiple finite element analyses as regression points to approximate the FEA solution. The surrogate models can then be mapped to their respective geometric locations in real-time. Solution contours display the results of the FEA calculations and are updated in real-time as the parameters of the design model change.
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Lee, Chang-Hwa 1957. "Analysis of approaches to synchronous faults simulation by surrogate propagation." Thesis, The University of Arizona, 1988. http://hdl.handle.net/10150/276771.

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This thesis describes a new simulation technique, Synchronous Faults Simulation by Surrogate with Exception, first proposed by Dr. F. J. Hill and has been initiated under the direction of Xiolin Wang. This paper reports early results of that project. The Sequential Circuit Test Sequence System, SCIRTSS, is an automatic test generation system which is developed in University of Arizona which will be used as a target to compare against the results of the new simulator. The major objective of this research is to analyze the results obtained by using the new simulator SFSSE against the results obtained by using the parallel simulator SCIRTSS. The results are listed in this paper to verify superiority of the new simulation technique.
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Shashidhar, Akhil. "Generalized Volterra-Wiener and surrogate data methods for complex time series analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/41619.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (leaves 133-150).
This thesis describes the current state-of-the-art in nonlinear time series analysis, bringing together approaches from a broad range of disciplines including the non-linear dynamical systems, nonlinear modeling theory, time-series hypothesis testing, information theory, and self-similarity. We stress mathematical and qualitative relationships between key algorithms in the respective disciplines in addition to describing new robust approaches to solving classically intractable problems. Part I presents a comprehensive review of various classical approaches to time series analysis from both deterministic and stochastic points of view. We focus on using these classical methods for quantification of complexity in addition to proposing a unified approach to complexity quantification encapsulating several previous approaches. Part II presents robust modern tools for time series analysis including surrogate data and Volterra-Wiener modeling. We describe new algorithms converging the two approaches that provide both a sensitive test for nonlinear dynamics and a noise-robust metric for chaos intensity.
by Akhil Shashidhar.
M.Eng.
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Bilicz, Sandor. "Application of Design-of-Experiment Methods and Surrogate Models in Electromagnetic Nondestructive Evaluation." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00601753.

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Le contrôle non destructif électromagnétique (CNDE) est appliqué dans des domaines variés pour l'exploration de défauts cachés affectant des structures. De façon générale, le principe peut se poser en ces termes : un objet inconnu perturbe un milieu hôte donné et illuminé par un signal électromagnétique connu, et la réponse est mesurée sur un ou plusieurs récepteurs de positions connues. Cette réponse contient des informations sur les paramètres électromagnétiques et géométriques des objets recherchés et toute la difficulté du problème traité ici consiste à extraire ces informations du signal obtenu. Plus connu sous le nom de " problèmes inverses ", ces travaux s'appuient sur une résolution appropriée des équations de Maxwell. Au " problème inverse " est souvent associé le " problème direct " complémentaire, qui consiste à déterminer le champ électromagnétique perturbé connaissant l'ensemble des paramètres géométriques et électromagnétiques de la configuration, défaut inclus. En pratique, cela est effectué via une modélisation mathématique et des méthodes numériques permettant la résolution numérique de tels problèmes. Les simulateurs correspondants sont capables de fournir une grande précision sur les résultats mais à un coût numérique important. Sachant que la résolution d'un problème inverse exige souvent un grand nombre de résolution de problèmes directs successifs, cela rend l'inversion très exigeante en termes de temps de calcul et de ressources informatiques. Pour surmonter ces challenges, les " modèles de substitution " qui imitent le modèle exact peuvent être une solution alternative intéressante. Une manière de construire de tels modèles de substitution est d'effectuer un certain nombre de simulations exactes et puis d'approximer le modèle en se basant sur les données obtenues. Le choix des simulations (" prototypes ") est normalement contrôlé par une stratégie tirée des outils de méthodes de " plans d'expérience numérique ". Dans cette thèse, l'utilisation des techniques de modélisation de substitution et de plans d'expérience numérique dans le cadre d'applications en CNDE est examinée. Trois approches indépendantes sont présentées en détail : une méthode d'inversion basée sur l'optimisation d'une fonction objectif et deux approches plus générales pour construire des modèles de substitution en utilisant des échantillonnages adaptatifs. Les approches proposées dans le cadre de cette thèse sont appliquées sur des exemples en CNDE par courants de Foucault
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Peesapati, Lakshmi Narasimham. "Methods To evaluate the effectiveness of certain surrogate measures to assess safety of opposing left-turn interactions." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52324.

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Highway safety evaluation has traditionally been performed using crash data. However crash data based safety analysis has limitations in terms of timeliness and efficiency. Previous studies show that the use of surrogate safety data allows for earlier evaluation of safety in comparison to the significantly longer time horizon required for collecting crash data. However, the predictive capability of surrogate measures is an area of ongoing research. Previous studies have often resulted in inconsistent findings in the relationship between surrogates and crashes, one of the primary reasons being inconsistent definitions of a conflict. This study evaluated the effectiveness of certain surrogate measures (Acceleration-Deceleration profile, intersection entering speed of through vehicles, and Post Encroachment Time (PET)) in assessing the safety of opposing left-turn interactions at 4-legged signalized intersections by collection of time resolved video from eighteen selected intersections throughout Georgia. Overall, this research demonstrated that surrogate measures can be effective in safety evaluation, specifically demonstrating the use of PET as a surrogate for crashes between left-turning vehicles and opposing through vehicles. The analysis of data found that the selected surrogate threshold is critical to the effectiveness of any surrogate measure. For example, the required PET threshold was found to be as low as 1 second to identify high crash intersections, significantly lower than the commonly reported 3 second threshold. Non-parametric rank analysis methods and generalized linear modeling techniques were used to model PET with other intersection and traffic characteristics to demonstrate the degree to which these surrogates can be used to identify potential high-crash intersections without resorting to a crash history. Finally, the effectiveness of PET and its assistance to decision makers is also been demonstrated through an example that helped find errors in reported crash data.
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Thomas, Sarah Nichole. "Decisions to Seek and Share: A Mixed Methods Approach to Understanding Caregivers Surrogate Information Acquisition Behaviors." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595545894518707.

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Isaacs, Amitay Engineering &amp Information Technology Australian Defence Force Academy UNSW. "Development of optimization methods to solve computationally expensive problems." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43758.

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Evolutionary algorithms (EAs) are population based heuristic optimization methods used to solve single and multi-objective optimization problems. They can simultaneously search multiple regions to find global optimum solutions. As EAs do not require gradient information for the search, they can be applied to optimization problems involving functions of real, integer, or discrete variables. One of the drawbacks of EAs is that they require evaluations of numerous candidate solutions for convergence. Most real life engineering design optimization problems involve highly nonlinear objective and constraint functions arising out of computationally expensive simulations. For such problems, the computation cost of optimization using EAs can become quite prohibitive. This has stimulated the research into improving the efficiency of EAs reported herein. In this thesis, two major improvements are suggested for EAs. The first improvement is the use of spatial surrogate models to replace the expensive simulations for the evaluation of candidate solutions, and other is a novel constraint handling technique. These modifications to EAs are tested on a number of numerical benchmarks and engineering examples using a fixed number of evaluations and the results are compared with basic EA. addition, the spatial surrogates are used in the truss design application. A generic framework for using spatial surrogate modeling, is proposed. Multiple types of surrogate models are used for better approximation performance and a prediction accuracy based validation is used to ensure that the approximations do not misguide the evolutionary search. Two EAs are proposed using spatial surrogate models for evaluation and evolution. For numerical benchmarks, the spatial surrogate assisted EAs obtain significantly better (even orders of magnitude better) results than EA and on an average 5-20% improvements in the objective value are observed for engineering examples. Most EAs use constraint handling schemes that prefer feasible solutions over infeasible solutions. In the proposed infeasibility driven evolutionary algorithm (IDEA), a few infeasible solutions are maintained in the population to augment the evolutionary search through the infeasible regions along with the feasible regions to accelerate convergence. The studies on single and multi-objective test problems demonstrate the faster convergence of IDEA over EA. In addition, the infeasible solutions in the population can be used for trade-off studies. Finally, discrete structures optimization (DSO) algorithm is proposed for sizing and topology optimization of trusses. In DSO, topology optimization and sizing optimization are separated to speed up the search for the optimum design. The optimum topology is identified using strain energy based material removal procedure. The topology optimization process correctly identifies the optimum topology for 2-D and 3-D trusses using less than 200 function evaluations. The sizing optimization is performed later to find the optimum cross-sectional areas of structural elements. In surrogate assisted DSO (SDSO), spatial surrogates are used to accelerate the sizing optimization. The truss designs obtained using SDSO are very close (within 7% of the weight) to the best reported in the literature using only a fraction of the function evaluations (less than 7%).
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Books on the topic "Surrogate methods"

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Alonso, Ariel. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Boca Raton : CRC Press, 2017.: Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/9781315372662.

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Forrester, Alexander I. J. Surrogate models in engineering design: A practical guide. Chichester, West Sussex, England: J. Wiley, 2008.

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Molenberghs, Geert, Marc Buyse, Tomasz Burzykowski, Ariel Alonso, and Theophile Bigirumurame. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.

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Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.

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Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.

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Molenberghs, Geert, Marc Buyse, Tomasz Burzykowski, Ariel Alonso, and Theophile Bigirumurame. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.

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Molenberghs, Geert, Marc Buyse, Tomasz Burzykowski, Ariel Alonso, and Theophile Bigirumurame. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2016.

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Alonso, Ariel. Applied Surrogate Endpoint Evaluation Methods with SAS and R. Taylor & Francis Group, 2020.

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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Entropy and Surrogate Testing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0005.

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Reconstructing real-world system dynamics from time series data on a single variable is challenging because real-world data often exhibit a highly volatile and irregular appearance potentially driven by several diverse factors. NLTS methods help eliminate less likely drivers of dynamic irregularity. We set a benchmark for regular behavior by investigating how linear systems of ODEs are restricted to exponential and periodic dynamics, and illustrating how irregular behavior can arise if regular linear dynamics are corrupted with noise or shift over time (i.e., nonstationarity). We investigate how data can be pre-processed to control for the noise and nonstationarity potentially camouflaging nonlinear deterministic drivers of observed complexity. We can apply signal-detection methods, such as Singular Spectrum Analysis (SSA), to separate signal from noise in the data, and test the signal for nonstationarity potentially corrected with SSA. SSA measures signal strength which provides a useful initial indicator of whether we should continue searching for endogenous nonlinear drivers of complexity. We begin diagnosing deterministic structure in an isolated signal by attempting to reconstructed a shadow attractor. Finally, we use the classic Lorenz equations to illustrate how a deterministic nonlinear system of ODEs with at least three equations can generate observed irregular dynamics endogenously without aid of exogenous shocks or nonstationary dynamics.
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(Editor), Michael E. Burczynski, and John C. Rockett (Editor), eds. Surrogate Tissue Analysis: Genomic, Proteomic, and Metabolomic Approaches. CRC, 2005.

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Book chapters on the topic "Surrogate methods"

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Koziel, Slawomir, David Echeverría Ciaurri, and Leifur Leifsson. "Surrogate-Based Methods." In Computational Optimization, Methods and Algorithms, 33–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20859-1_3.

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Fleming, Thomas R., Victor DeGruttola, and David L. Demets. "Surrogate Endpoints." In Methods and Applications of Statistics in Clinical Trials, 878–86. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118596005.ch74.

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Qu, Yongming. "Surrogate Biomarkers." In Statistical Methods in Biomarker and Early Clinical Development, 39–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31503-0_3.

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Rehbach, Frederik. "Methods/Contributions." In Enhancing Surrogate-Based Optimization Through Parallelization, 29–94. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30609-9_3.

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Jiang, Ping, Qi Zhou, and Xinyu Shao. "Verification Methods for Surrogate Models." In Surrogate Model-Based Engineering Design and Optimization, 89–113. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0731-1_5.

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Molenberghs, Geert, Ziv Shkedy, Burzykowski Tomasz, Marc Buyse, Ariel Alonso Abad, and Wim Van der Elst. "Evaluation of Surrogate Endpoints." In Handbook of Statistical Methods for Randomized Controlled Trials, 567–600. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315119694-26.

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Gao, Yuehua, Lih-Sheng Turng, Peng Zhao, and Huamin Zhou. "Optimization Methods Based on Surrogate Models." In Computer Modeling for Injection Molding, 293–312. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118444887.ch11.

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Yang, Kai, and Katta G. Murty. "Surrogate Constraint Methods for Linear Inequalities." In Combinatorial Optimization, 19–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77489-8_2.

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Aumann, Quirin, Peter Benner, Jens Saak, and Julia Vettermann. "Model Order Reduction Strategies for the Computation of Compact Machine Tool Models." In Lecture Notes in Production Engineering, 132–45. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_10.

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AbstractThe deviation of the tool center point (TCP) of a machine tool from its desired location needs to be assessed correctly to ensure an accurate and safe operation of the machine. A major source of TCP deviation are thermal loads, which are constantly changing during operation. Numerical simulation models help predicting these loads, but are typically large and expensive to solve. Especially in (real-time feedback) control settings, but also to ensure an efficient design phase of machine tools, it is inevitable to use compact reduced-order surrogate models which approximate the behavior of the original system but are much less computationally expensive to evaluate. Model order reduction (MOR) methods generate computationally efficient surrogates. Classic intrusive methods require explicit access to the assembled system matrices. However, commercial software packages, which are typically used for the design of machine tools, do not always allow an unrestricted access to the required matrices. Non-intrusive data-driven methods compute surrogates requiring only input and output data of a dynamical system and are therefore independent of the discretization method. We evaluate the performance of such data-driven approaches to compute cheap-to-evaluate surrogate models of machine tools and compare their efficacy to intrusive MOR strategies. A focus is put on modeling the machine tool via individual substructures, which can be reduced independently of each other.
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Kansara, Saket, Sumeet Parashar, and Abdus Samad. "Chapter 3 Surrogate-Assisted Evolutionary Computing Methods." In Evolutionary Computation, 55–80. 3333 Mistwell Crescent, Oakville, ON L6L 0A2, Canada: Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388-4.

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Conference papers on the topic "Surrogate methods"

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Freire Neto, José Ilmar Cruz, and André Britto. "Surrogate Methods Applied to Hyperparameter Optimization Problem." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/eniac.2022.227594.

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Hyperparameters affects the performance of machine learning models. Hyperparameter optimization is an area that aims to find the best of them, but it deals with a considerable number of machine learning training, which can be slow. Thus, surrogates can be used to soften this slow process. This paper evaluates the performance of two surrogate methods, M1 and MARSAOP, applied to hyperparameter optimization. The surrogates are confronted with six hyperparameter optimization algorithms from the literature for classification and regression problems. Results indicate that the surrogate methods are faster than the traditional algorithms.
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Phelivan Soak, H., J. Wackers, R. Pellegrini, A. Serani, M. Diez, R. Perali, M. Sacher, et al. "Hydrofoil Optimization via Automated Multi-Fidelity Surrogate Models." In 10th Conference on Computational Methods in Marine Engineering. CIMNE, 2023. http://dx.doi.org/10.23967/marine.2023.136.

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Ranftl, Sascha, and Wolfgang von der Linden. "Bayesian Surrogate Analysis and Uncertainty Propagation." In International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/psf2021003006.

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Wackers, J., H. Pehlivan Solak, R. Pellegrini, A. Serani, and M. Díez. "Error estimation for surrogate models with noisy small-sized training sets." In VIII International Conference on Particle-Based Methods. CIMNE, 2023. http://dx.doi.org/10.23967/c.particles.2023.007.

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Alizadeh, Reza, Janet K. Allen, and Farrokh Mistree. "Surrogate Models and Time Series for Flow Prediction on the Red River Dam Network." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-88163.

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Abstract Surrogate models have been used to replace computationally expensive analysis models in engineering design problems. However, time-dependent variables and historical data are usually ignored in the surrogate modeling process. For instance, in a dam network design, using hydraulic simulations to estimate the water flow is computationally expensive, and the data is in the form of time series. So, we need time-dependent surrogate models to replace these simulations and manage this computational complexity. In this paper, we describe surrogate models to predict the amount of water flow into a reservoir. The challenge is that the flow is a time-dependent variable, and we need to incorporate time series into surrogate models. Thus, there are three contributions: (1) using surrogate modeling to predict flow for dam network design, (2) incorporating time series analysis in surrogate models for water network design, (3) using an ensemble of surrogates to increase the accuracy of prediction. We also demonstrate how to integrate surrogate models and machine learning with time series analysis for more accurate and faster prediction. Due to the availability of data, we use the Buffalo Reservoir in the Red River Basin as an example. Based on the time series data for flow, evaporation, precipitation, and maximum and minimum temperature, three surrogate models are used to examine the impact of integrating time series into surrogate models. These are multivariate autoregressive integrated moving average (MARIMA), a classic time series analysis method; artificial neural network (ANN), and random forest (RF) methods, two machine learning surrogate models. We use seven different time lags as features within an RF model, as an ensemble of surrogate models, and predict the flow for seven-time steps ahead. We successfully incorporate the time series data and particularly the concept of the time lag within surrogate models. We show that RF as the ensemble of surrogates provides more accurate predictions than the other two surrogate models. Although this method has been demonstrated for the Red River Basin, it could also be applied to designing anything in which time-dependent flow is an issue, for example, in biomedical applications, the management of manufacturing processes and product sales as well as any products in which fluid flow is an issue.
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Simion, Andrei, Michael Collins, and Cliff Stein. "Towards a Convex HMM Surrogate for Word Alignment." In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/d16-1051.

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Albert, Christopher G., Ulrich Callies, and Udo von Toussaint. "Surrogate-Enhanced Parameter Inference for Function-Valued Models." In International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/psf2021003011.

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De Villiers, Dirk. "RECENT ADVANCES IN SURROGATE MODELLING OF REFLECTOR ANTENNA SYSTEMS." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2111.6166.

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Jacobs, Jan Pieter, and Dirk De Villiers. "SURROGATE MODELING OF ANTENNA RADIATION CHARACTERISTICS BY GAUSSIAN PROCESSES." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2113.7849.

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Kotti, M., R. Gonzalez-Echevarria, E. Roca, R. Castro-Lopez, F. V. Fernandez, M. Fakhfakh, J. Sieiro, and J. M. Lopez-Villegas. "Surrogate models of Pareto-optimal planar inductors." In 2012 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD). IEEE, 2012. http://dx.doi.org/10.1109/smacd.2012.6339412.

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Reports on the topic "Surrogate methods"

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Stromer, Bobbi, Rebecca Crouch, Katrinka Wayne, Ashley Kimble, Jared Smith, and Anthony Bednar. Methods for simultaneous determination of 29 legacy and insensitive munition (IM) constituents in aqueous, soil-sediment, and tissue matrices by high-performance liquid chromatography (HPLC). Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/1168142105.

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Standard methods are in place for analysis of 17 legacy munitions compounds and one surrogate in water and soil matrices; however, several insensitive munition (IM) and degradation products are not part of these analytical procedures. This lack could lead to inaccurate determinations of munitions in environmental samples by either not measuring for IM compounds or using methods not designed for IM and other legacy compounds. This work seeks to continue expanding the list of target analytes currently included in the US Environmental Protection Agency (EPA) Method 8330B. This technical report presents three methods capable of detecting 29 legacy, IM, and degradation products in a single High Performance Liquid Chromatography (HPLC) method with either ultraviolet (UV)-visible absorbance detection or mass spectrometric detection. Procedures were developed from previously published works and include the addition of hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine (MNX); hexahydro-1,3-dinitroso-5-nitro-1,3,5-triazine (DNX); hexahydro-1,3,5-trinitroso-1,3,5-triazine (TNX); 2,4-diamino-6-nitrotoluene (2,4-DANT); and 2,6-diamino-4-nitrotoluene (2,6-DANT). One primary analytical method and two secondary (confirmation) methods were developed capable of detecting 29 analytes and two surrogates. Methods for high water concentrations (direct injection), low-level water concentrations (solid phase extraction), soil (solvent extraction), and tissue (solvent extraction) were tested for analyte recovery of the new compounds.
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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Walizer, Laura, Robert Haehnel, Luke Allen, and Yonghu Wenren. Application of multi-fidelity methods to rotorcraft performance assessment. Engineer Research and Development Center (U.S.), May 2024. http://dx.doi.org/10.21079/11681/48474.

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We present a Python-based multi-fidelity tool to estimate rotorcraft performance metrics. We use Gaussian-Process regression (GPR) methods to adaptively build a surrogate model using a small number of high-fidelity CFD points to improve estimates of performance metrics from a medium-fidelity comprehensive analysis model. To include GPR methods in our framework, we used the EmuKit Python package. Our framework adaptively chooses new high-fidelity points to run in regions where the model variance is high. These high-fidelity points are used to update the GPR model; convergence is reached when model variance is below a pre-determined level. To efficiently use our framework on large computer clusters, we implemented this in Galaxy Simulation Builder, an analysis tool that is designed to work on large parallel computing environments. The program is modular, and is designed to be agnostic to the number and names of dependent variables and to the number and identifying labels of the fidelity levels. We demonstrate our multi-fidelity modeling framework on a rotorcraft collective sweep (hover) simulation and compare the accuracy and time savings of the GPR model to that of a simulation run with CFD only.
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Liu, Tong, and Hadi Meidani. Artificial Intelligence for Optimal Truck Platooning: Impact on Autonomous Freight Delivery. Illinois Center for Transportation, August 2023. http://dx.doi.org/10.36501/0197-9191/23-017.

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The advancements in autonomous- and connected-vehicle technologies bring drastic changes in freight delivery. Vehicle-to-vehicle and vehicle-to-infrastructure communication has become a reality with the help of autonomous and connected vehicles. One of the most notable changes is the formation of truck platoons. Despite the numerous benefits of truck platooning, such as reduced fuel consumption and increased traffic efficiency, this approach requires a significant amount of computational resources to obtain aerodynamic performance under different scenarios. To overcome this challenge, a data-driven surrogate model was proposed to predict the drag force and fuel-consumption rate of truck platoons. The surrogate model improves computational efficiency, as compared to traditional methods, and provides a valuable tool for evaluating the performance of truck platoons. To demonstrate the benefits of truck platooning, a 161-km (100-mi) corridor in Illinois on I-57 highway was selected to conduct fuel-consumption analysis and delivery-cost analysis for a three-truck platoon. The results showed that the average fuel savings achieved can be up to 10%, depending on the headway between the trucks. The delivery cost of the truck platoon was reduced by 30%, as compared with conventional line-haul delivery. These findings highlighted the importance of truck platooning as a solution for reducing fuel consumption and improving delivery economy in the freight industry.
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Mudge, Christopher, Glenn Suir, and Benjamin Sperry. Unmanned aircraft systems and tracer dyes : potential for monitoring herbicide spray distribution. Engineer Research and Development Center (U.S.), October 2023. http://dx.doi.org/10.21079/11681/47705.

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Chemical control of nuisance aquatic vegetation has long been the most widely utilized management tool due to its high level of efficacy, limited environmental impacts, and relatively low cost. However, unprecise application of herbicides can lead to uncontrolled invasive plants and unintended management costs. Therefore, precision herbicide delivery techniques are being developed to improve invasive plant control and minimize impacts to non-target plants. These technological advancements have the potential to enhance aquatic ecosystem protection from invasive species while reducing associated management costs. Despite the benefits of using registered herbicides for aquatic plant control in efforts to restore aquatic habitats, their use is often misunderstood and opposed by public stakeholders. This can lead to significant challenges related to chemical control of nuisance aquatic vegetation. Thus, US Army Corps of Engineers (USACE) Districts seek improved methods to monitor and quantify the distribution (i.e., amount of herbicide retained on plant foliage compared to those deposited into the water column) of herbicides applied in aquatic systems. Monitoring herbicide movement in aquatic systems can be tedious and costly using standard analytical methods. However, since the inert fluorescent tracer dye Rhodamine WT (RWT) closely mimics product movement in the aquatic environment it has been used as a cost-effective surrogate for herbicides tracing. The use of RWT (or other inert tracer dyes) can be an efficient way to quantify herbicide retention and deposition following foliar treatments. However, the collection of operational spray deposition data in large populations of invasive floating and emergent plant stands is labor intensive and costly. One proposed solution is the use of remote sensing methods as an alternative to traditional in situ samples. Specifically, using unmanned aircraft systems (UAS) in conjunction with RWT could provide more efficient monitoring and quantification of herbicide spray distribution and in-water concentrations when using RWT in combination with herbicides. A better understanding of UAS capabilities and limitations is key as this technology is being explored for improved and integrated management of aquatic plants in the U.S. This technical note (TN) provides a review of literature to assess the state of knowledge and technologies that can assist USACE Districts and partners with tracking herbicide movement (using RWT as a surrogate or additive), which could improve operational monitoring, thus reducing the level of uncertainty related to chemical applications and non-target impacts, and thus improve management in aquatic systems.
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Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel, and Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.
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7

Field, Richard V. ,. Jr, and .). A decision-theoretic method for surrogate model selection. Office of Scientific and Technical Information (OSTI), June 2005. http://dx.doi.org/10.2172/882352.

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8

Burke, J., L. Bernstein, J. Escher, L. Ahle, J. Church, F. Dietrich, K. Moody, et al. Deducing the 237U(n,f) cross-section using the Surrogate Ratio Method. Office of Scientific and Technical Information (OSTI), August 2005. http://dx.doi.org/10.2172/883605.

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9

Crouch, Rebecca, Jared Smith, Bobbi Stromer, Christian Hubley, Samuel Beal, Guilherme Lotufo, Afrachanna Butler, et al. Methods for simultaneous determination of legacy and insensitive munition (IM) constituents in aqueous, soil/sediment, and tissue matrices. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41720.

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Currently, no standard method exists for analyzing insensitive munition (IM) compounds in environmental matrices, with or without concurrent legacy munition compounds, resulting in potentially inaccurate determinations. The primary objective of this work was to develop new methods of extraction, pre-concentration, and analytical separation/quantitation of 17 legacy munition compounds along with several additional IM compounds, IM breakdown products, and other munition compounds that are not currently included in U.S. Environmental Protection Agency (EPA) Method 8330B. Analytical methods were developed to enable sensitive, simultaneous detection and quantitation of the 24 IM and legacy compounds, including two orthogonal high-performance liquid chromatography (HPLC) column separations with either ultraviolet (UV) or mass spectrometric (MS) detection. Procedures were developed for simultaneous extraction of all 24 analytes and two surrogates (1,2-dinitrobenzene, 1,2-DNB; o-NBA) from high- and low-level aqueous matrices and solid matrices, using acidification, solid phase extraction (SPE), or solvent extraction (SE), respectively. The majority of compounds were recovered from four tissue types within current limits for solids, with generally low recovery only for Tetryl (from 4 to 62%). A preparatory chromatographic interference removal procedure was adapted for tissue extracts, as various analytical interferences were observed for all studied tissue types.
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10

Escher, J. Benchmark and Assessment of the Surrogate Reaction Method for Determining Unknown (n,n') and (n,2n) Reaction Cross Sections. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1884627.

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