Relevant bibliographies by topics / Surrogate dynamics

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Academic literature on the topic 'Surrogate dynamics'

Author: Grafiati
Published: 30 November 2024

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Journal articles on the topic "Surrogate dynamics"

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Huang, C.-K., Q. Tang, Y. K. Batygin, O. Beznosov, J. Burby, A. Kim, S. Kurennoy, T. Kwan, and H. N. Rakotoarivelo. "Symplectic neural surrogate models for beam dynamics." Journal of Physics: Conference Series 2687, no. 6 (January 1, 2024): 062026. http://dx.doi.org/10.1088/1742-6596/2687/6/062026.

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Abstract Development of robust machine-learning (ML) based surrogates for particle accelerators can significantly benefit the modeling, design, optimization, monitoring and control of such accelerators. It is desirable that the surrogate models embed fundamental physical constraints to the interaction and dynamics of the beams, for which an accelerator must be designed to operate upon. We implement and train a class of phase space structure-preserving neural networks — Henon Neural Networks (HenonNets) [1], for nonlinear beam dynamics problems. It is demonstrated that the trained HenonNet model predicts the beam transfer matrix to a reasonably good accuracy while strongly maintaining the symplecticity. To explore such model’s applicability and flexibility for high brightness or intensity beams, we further test it with beam dynamics in the presence of electrostatic and radiative collective effects. Our results indicate that HenonNet may be used as a base ML model for the surrogate of complex beam dynamics, thus opening up a wide range of applications.
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NAKAMURA, TOMOMICHI, and MICHAEL SMALL. "APPLYING THE METHOD OF SMALL–SHUFFLE SURROGATE DATA: TESTING FOR DYNAMICS IN FLUCTUATING DATA WITH TRENDS." International Journal of Bifurcation and Chaos 16, no. 12 (December 2006): 3581–603. http://dx.doi.org/10.1142/s0218127406016999.

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Recently, a new surrogate method, the Small–Shuffle (SS) surrogate method, has been proposed to investigate whether there is some kind of dynamics in irregular fluctuations, even if they are modulated by long term trends or periodicities. This situation is theoretically incompatible with the assumption underlying previously proposed surrogate methods. We apply the SS surrogate method to a variety of simulated data with known dynamics and actual time series with unknown dynamics.
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Koutsoupakis, Josef, and Dimitrios Giagopoulos. "Drivetrain Response Prediction Using AI-based Surrogate and Multibody Dynamics Model." Machines 11, no. 5 (April 28, 2023): 514. http://dx.doi.org/10.3390/machines11050514.

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Numerical models, such as multibody dynamics ones, are broadly used in various engineering applications, either as an integral part of the preliminary design of a product or simply to analyze its behavior. Aiming to increase the accuracy and potential of these models, complex mechanisms are constantly being added to existing methods of simulation, leading to powerful modelling frameworks that are able to simulate most mechanical systems. This increase in accuracy and flexibility, however, comes at a great computational cost. To mitigate the issue of high computation times, surrogates, such as reduced order models, have traditionally been used as cheaper alternatives, allowing for much faster simulations at the cost of introducing some error to the overall process. More recently, advancements in Artificial Intelligence have also allowed for the introduction of Artificial Intelligence-based models in the field of surrogates. While still undergoing development, these Artificial Intelligence based methodologies seem to be a potentially good alternative to the high-fidelity/burden models. To this end, an Artificial Intelligence-based surrogate comprised of Artificial Neural Networks as a means of predicting the response of dynamic mechanical systems is presented in this work, with application to a non-linear experimental gear drivetrain. The model utilizes Recurrent Neural Networks to accurately capture the system’s response and is shown to yield accurate results, especially in the feature space. This methodology can provide an alternative to the traditional model surrogates and find application in multiple fields such as system optimization or data mining.
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Charles, Giovanni, Timothy M. Wolock, Peter Winskill, Azra Ghani, Samir Bhatt, and Seth Flaxman. "Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14170–77. http://dx.doi.org/10.1609/aaai.v37i12.26658.

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Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference.
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Chen, Menghui, Xiaoshu Gao, Cheng Chen, Tong Guo, and Weijie Xu. "A Comparative Study of Meta-Modeling for Response Estimation of Stochastic Nonlinear MDOF Systems Using MIMO-NARX Models." Applied Sciences 12, no. 22 (November 14, 2022): 11553. http://dx.doi.org/10.3390/app122211553.

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Complex dynamic behavior of nonlinear structures makes it challenging for uncertainty analysis through Monte Carlo simulations (MCS). Surrogate modeling presents an efficient and accurate computational alternative for a large number of MCS. The previous study has demonstrated that the multi-input multi-output nonlinear autoregressive with exogenous input (MIMO-NARX) model provides good discrete-time representations of deterministic nonlinear multi-degree-of-freedom (MDOF) structural dynamic systems. Model order reduction (MOR) is executed to eliminate insignificant modes to reduce the computational burden due to too many degrees of freedom. In this study, the MIMO-NARX strategy is integrated with different meta-modeling techniques for uncertainty analysis. Different meta-models including Kriging, polynomial chaos expansion (PCE), and arbitrary polynomial chaos (APC) are used to surrogate the NARX coefficients for system uncertainties. A nine-DOF structure is used as an MDOF dynamic system to evaluate different meta-models for the MIMO-NARX. Good fitness of statistical responses is observed between the MCS results of the original system and all surrogated MIMO-NARX predictions. It is demonstrated that the APC-NARX model with the advantage of being data-driven is the most efficient and accurate tool for uncertainty quantification of nonlinear structural dynamics.
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Liu, Shizhong, Ziyao Wang, Jingwen Chen, Rui Xu, and Dong Ming. "The Estimation of Knee Medial Force with Substitution Parameters during Walking and Turning." Sensors 24, no. 17 (August 29, 2024): 5595. http://dx.doi.org/10.3390/s24175595.

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Purpose: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. Methods: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody. Linear regression was used to predict the peak of knee medial force with the peaks of the surrogate parameters and walking speed. Results: There was no significant difference in walking speed among these three tasks. The peak knee adduction moment (pKAM) was a significant predictor of the peak knee medial force (pKMF) for SW, ST, and CT (p < 0.001), while the peak knee flexion moment (pKFM) was only a significant predictor of the pKMF for SW (p = 0.034). The statistical analysis showed that the pKMF increased, while the pKFM and the peak knee adduction angle (pKAA) decreased significantly during CT compared to those of SW and ST (p < 0.001). The correlation analysis indicated that the knee parameters during SW and ST were quite similar. Conclusions: This study investigated the relationship between knee medial force and some surrogate parameters during walking and turning. KAM was still the best surrogate parameter for SW, ST, and CT. It is necessary to consider the type of movement when comparing the surrogate predictors of knee medial force, as the prediction equations differ significantly among movement types.
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Gong, Xu, Zhengqi Gu, and Zhenlei Li. "Surrogate model for aerodynamic shape optimization of a tractor-trailer in crosswinds." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 226, no. 10 (May 9, 2012): 1325–39. http://dx.doi.org/10.1177/0954407012442295.

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A surrogate model-based aerodynamic shape optimization method applied to the wind deflector of a tractor-trailer is presented in this paper. The aerodynamic drag coefficient of the tractor-trailer with and without the wind deflector subjected to crosswinds is analyzed. The numerical results show that the wind deflector can decrease drag coefficient. Four parameters are used to describe the wind deflector geometry: width, length, height, and angle. A 30-level design of experiments study using the optimal Latin hypercube method was conducted to analyze the sensitivity of the design variables and build a database to set up the surrogate model. The surrogate model was constructed based on the Kriging interpolation technique. The fitting precision of the surrogate model was examined using computational fluid dynamics and certified using a surrogate model simulation. Finally, a multi-island genetic algorithm was used to optimize the shape of the wind deflector based on the surrogate model. The tolerance between the results of the computational fluid dynamics simulation and the surrogate model was only 0.92% when using the optimal design variables, and the aerodynamic drag coefficient decreased by 4.65% compared to the drag coefficient of the tractor-trailer installed with the original wind deflector. The effect of the optimal shape of the wind deflector was validated by computational fluid dynamics and wind tunnel experiment.
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She, N., and D. Basketfield. "Streamflow dynamics at the Puget Sound, Washington: application of a surrogate data method." Nonlinear Processes in Geophysics 12, no. 4 (May 3, 2005): 461–69. http://dx.doi.org/10.5194/npg-12-461-2005.

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Abstract. Recent progress in nonlinear dynamic theory has inspired hydrologists to apply innovative nonlinear time series techniques to the analysis of streamflow data. However, regardless of the method employed to analyze streamflow data, the first step should be the identification of underlying dynamics using one or more methods that could distinguish between linear and nonlinear, deterministic and stochastic processes from data itself. In recent years a statistically rigorous framework to test whether or not the examined time series is generated by a Gaussian (linear) process undergoing a possibly nonlinear static transform is provided by the method of surrogate data. The surrogate data, generated to represent the null hypothesis, are compared to the original data under a nonlinear discriminating statistic in order to reject or approve the null hypothesis. In recognition of this tendency, the method of "surrogate data" is applied herein to determine the underlying linear stochastic or nonlinear deterministic nature of daily streamflow data observed from the central basin of Puget Sound, and as applicable, distinguish between the static or dynamic nonlinearity of the data in question.
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Glaz, Bryan, Li Liu, Peretz P. Friedmann, Jeremy Bain, and Lakshmi N. Sankar. "A Surrogate-Based Approach to Reduced-Order Dynamic Stall Modeling." Journal of the American Helicopter Society 57, no. 2 (April 1, 2012): 1–9. http://dx.doi.org/10.4050/jahs.57.022002.

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The surrogate-based recurrence framework (SBRF) approach to reduced-order dynamic stall modeling associated with pitching/plunging airfoils subject to fixed or time-varying freestream Mach numbers is described. The SBRF is shown to effectively mimic full-order two-dimensional computational fluid dynamics solutions for unsteady lift, moment, and drag, but at a fraction of the computational cost. In addition to accounting for realistic helicopter rotor blade dynamics, it is shown that the SBRF can model advancing rotor shock induced separation as well as retreating blade stall associated with excessive angles of attack. Therefore, the SBRF is ideally suited for a variety of rotary-wing aeroelasticity and active/passive design optimization studies that require high-fidelity aerodynamic response solutions with minimal computational expense.
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MAKINO, Kohei, Makoto MIWA, Kohei SHINTANI, Atsuji ABE, and Yutaka SASAKI. "Surrogate modeling of vehicle dynamics using deep learning." Proceedings of Design & Systems Conference 2019.29 (2019): 2209. http://dx.doi.org/10.1299/jsmedsd.2019.29.2209.

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Dissertations / Theses on the topic "Surrogate dynamics"

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Koch, Christiane. "Quantum dissipative dynamics with a surrogate Hamiltonian." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2002. http://dx.doi.org/10.18452/14816.

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Diese Dissertation untersucht Quantensysteme in kondensierter Phase, welche mit ihrer Umgebung wechselwirken und durch ultrakurze Laserpulse angeregt werden. Die Zeitskalen der verschiedenen beteiligten Prozessen lassen sich bei solchen Problemen nicht separieren, weshalb die Standardmethoden zur Behandlung offener Quantensysteme nicht angewandt werden können. Die Methode des Surrogate Hamiltonian stellt ein Beispiel neuer Herangehensweisen an dissipative Quantendynamik dar. Die Weiterentwicklung der Methode und ihre Anwendung auf Phänomene, die zur Zeit experimentell untersucht werden, stehen im Mittelpunkt dieser Arbeit. Im ersten Teil der Arbeit werden die einzelnen dissipativen Prozesse klassifiziert und diskutiert. Insbesondere wird ein Modell der Dephasierung in die Methode des Surrogate Hamiltonian eingeführt. Dies ist wichtig für zukünftige Anwendungen der Methode, z.b. auf kohärente Kontrolle oder Quantencomputing. Diesbezüglich hat der Surrogate Hamiltonian einen großen Vorteil gegenüber anderen zur Verfügung stehenden Methoden dadurch, daß er auf dem Spin-Bad, d.h. auf einer vollständig quantenmechanischen Beschreibung der Umgebung, beruht. Im nächsten Schritt wird der Surrogate Hamiltonian auf ein Standardproblem für Ladungstransfer in kondensierter Phase angewandt, zwei nichtadiabatisch gekoppelte harmonische Oszillatoren, die in ein Bad eingebettet sind. Dieses Modell stellt eine große Vereinfachung von z.B. einem Molekül in Lösung dar, es dient hier jedoch als Testbeispiel für die theoretische Beschreibung eines prototypischen Ladungstransferereignisses. Alle qualitativen Merkmale eines solchen Experimentes können wiedergegeben und Defizite früherer Behandlungen identifiziert werden. Ultraschnelle Experimente beobachten Reaktionsdynamik auf der Zeitskala von Femtosekunden. Dies kann besonders gut durch den Surrogate Hamiltonian als einer Methode, die auf einer zeitabhängigen Beschreibung beruht, erfaßt werden. Die Kombination der numerischen Lösung der zeitabhängigen Schrödingergleichung mit der Wignerfunktion, die die Visualisierung eines Quantenzustands im Phasenraum ermöglicht, gestattet es, dem Ladungstransferzyklus intuitiv Schritt für Schritt zu folgen. Der Nutzen des Surrogate Hamiltonian wird weiterhin durch die Verbindung mit der Methode der Filterdiagonalisierung erhöht. Dies gestattet es, aus mit dem Surrogate Hamiltonian nur für relative kurze Zeite konvergierte Erwartungswerten Ergebnisse in der Frequenzdomäne zu erhalten. Der zweite Teil der Arbeit beschäftigt sich mit der theoretischen Beschreibung der laserinduzierten Desorption kleiner Moleküle von Metalloxidoberflächen. Dieses Problem stellt ein Beispiel dar, in dem alle Aspekte mit derselben methodischen Genauigkeit beschrieben werden, d.h. ab initio Potentialflächen werden mit einem mikroskopischen Modell für die Anregungs- und Relaxationsprozesse verbunden. Das Modell für die Wechselwirkung zwischen angeregtem Adsorbat-Substrat-System und Elektron-Loch-Paaren des Substrats beruht auf einer vereinfachten Darstellung der Elektron-Loch-Paare als ein Bad aus Dipolen und auf einer Dipol-Dipol-Wechselwirkung zwischen System und Bad. Alle Parameter können aus Rechnungen zur elektronischen Struktur abgeschätzt werden. Desorptionswahrscheinlichkeiten und Desorptionsgeschwindigkeiten werden unabhängig voneinander im experimentell gefundenen Bereich erhalten. Damit erlaubt der Surrogate Hamiltonian erstmalig eine vollständige Beschreibung der Photodesorptionsdynamik auf ab initio-Basis.
This thesis investigates condensed phase quantum systems which interact with their environment and which are subject to ultrashort laser pulses. For such systems the timescales of the involved processes cannot be separated, and standard approaches to treat open quantum systems fail. The Surrogate Hamiltonian method represents one example of a number of new approaches to address quantum dissipative dynamics. Its further development and application to phenomena under current experimental investigation are presented. The single dissipative processes are classified and discussed in the first part of this thesis. In particular, a model of dephasing is introduced into the Surrogate Hamiltonian method. This is of importance for future work in fields such as coherent control and quantum computing. In regard to these subjects, it is a great advantage of the Surrogate Hamiltonian over other available methods that it relies on a spin, i.e. a fully quantum mechanical description of the bath. The Surrogate Hamiltonian method is applied to a standard model of charge transfer in condensed phase, two nonadiabatically coupled harmonic oscillators immersed in a bath. This model is still an oversimplification of, for example, a molecule in solution, but it serves as testing ground for the theoretical description of a prototypical ultrafast pump-probe experiment. All qualitative features of such an experiment are reproduced and shortcomings of previous treatments are identified. Ultrafast experiments attempt to monitor reaction dynamics on a femtosecond timescale. This can be captured particularly well by the Surrogate Hamiltonian as a method based on a time-dependent picture. The combination of the numerical solution of the time-dependent Schrödinger equation with the phase space visualization given by the Wigner function allows for a step by step following of the sequence of events in a charge transfer cycle in a very intuitive way. The utility of the Surrogate Hamiltonian is furthermore significantly enhanced by the incorporation of the Filter Diagonalization method. This allows to obtain frequency domain results from the dynamics which can be converged within the Surrogate Hamiltonian approach only for comparatively short times. The second part of this thesis is concerned with the theoretical treatment of laser induced desorption of small molecules from oxide surfaces. This is an example which allows for a description of all aspects of the problem with the same level of rigor, i.e. ab initio potential energy surfaces are combined with a microscopic model for the excitation and relaxation processes. This model of the interaction between the excited adsorbate-substrate complex and substrate electron-hole pairs relies on a simplified description of the electron-hole pairs as a bath of dipoles, and a dipole-dipole interaction between system and bath. All parameters are connected to results from electronic structure calculations. The obtained desorption probabilities and desorption velocities are simultaneously found to be in the right range as compared to the experimental results. The Surrogate Hamiltonian approach therefore allows for a complete description of the photodesorption dynamics on an ab initio basis for the first time.
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Hibbs, Ryan E. "Conformational dynamics of the acetylcholine binding protein, a Nicotinic receptor surrogate." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3237010.

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Thesis (Ph. D.)--University of California, San Diego, 2006.
Title from first page of PDF file (viewed December 8, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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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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Millard, Daniel C. "Identification and control of neural circuit dynamics for natural and surrogate inputs in-vivo." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53405.

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A principal goal of neural engineering is to control the activation of neural circuits across space and time. The ability to control neural circuits with surrogate inputs is needed for the development of clinical neural prostheses and the experimental interrogation of connectivity between brain regions. Electrical stimulation provides a clinically viable method for activating neural tissue and the emergence of optogenetic stimulation has redefined the limitations on stimulating neural tissue experimentally. However, it remains poorly understood how these tools activate complex neural circuits. The goal of this proposed project was to gain a greater understanding of how to control the activity of neural circuits in-vivo using a combination of experimental and computational approaches. Voltage sensitive dye imaging was used to observe the spatiotemporal activity within the rodent somatosensory cortex in response to systematically varied patterns of sensory, electrical, and optogenetic stimulation. First, the cortical response to simple patterns of sensory and artificial stimuli was characterized and modeled, revealing distinct neural response properties due to the differing synchrony with which the neural circuit was engaged. Then, we specifically designed artificial stimuli to improve the functional relevance of the resulting downstream neural responses. Finally, through direct optogenetic modulation of thalamic state, we demonstrate control of the nonlinear propagation of neural activity within the thalamocortical circuit. The combined experimental and computational approach described in this thesis provides a comprehensive description of the nonlinear dynamics of the thalamocortical circuit to surrogate stimuli. Together, the characterization, modeling, and overall control of downstream neural activity stands to inform the development of central nervous system sensory prostheses, and more generally provides the initial tools and framework for the control of neural activity in-vivo.
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Segee, Molly Catherine. "Surrogate Models for Transonic Aerodynamics for Multidisciplinary Design Optimization." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71321.

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Multidisciplinary design optimization (MDO) requires many designs to be evaluated while searching for an optimum. As a result, the calculations done to evaluate the designs must be quick and simple to have a reasonable turn-around time. This makes aerodynamic calculations in the transonic regime difficult. Running computational fluid dynamics (CFD) calculations within the MDO code would be too computationally expensive. Instead, CFD is used outside the MDO to find two-dimensional aerodynamic properties of a chosen airfoil shape, BACJ, at a number of points over a range of thickness-to-chord ratios, free-stream Mach numbers, and lift coefficients. These points are used to generate surrogate models which can be used for the two-dimensional aerodynamic calculations required by the MDO computational design environment. Strip theory is used to relate these two-dimensional results to the three-dimensional wing. Models are developed for the center of pressure location, the lift curve slope, the wave drag, and the maximum allowable lift coefficient before buffet. These models have good agreement with the original CFD results for the airfoil. The models are integrated into the aerodynamic and aeroelastic sections of the MDO code.
Master of Science
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Minsavage, Kaitlyn Emily. "Neural Networks as Surrogates for Computational Fluid Dynamics Predictions of Hypersonic Flows." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610017352981371.

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Lagerstrom, Tiffany. "All in the Family: The Role of Sibling Relationships as Surrogate Attachment Figures." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/scripps_theses/1138.

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While several studies have analyzed the impact of mother-child attachment security on the child’s emotion regulation abilities, few studies have proposed interventions to help children improve emotion regulation abilities in the presence of an insecure mother-child attachment. This current study extends previous findings about the influence of mother-child attachment on the child’s emotion regulation abilities and contributes new research in determining whether an older sibling can moderate this effect. This study predicts that across points of assessments: 18 months, 5 years, 10 years, and 15 years, the quality of mother-child attachment security will influence the child’s performance on an emotion regulation task, such that securely attached children will demonstrate the most persistence and least distress, children with Anxious-Avoidant attachment will demonstrate the least persistence, and children with Anxious-Ambivalent will demonstrate the most distress. If, at any point, the child develops an insecure relationship with the mother and a secure relationship with the older sibling, the child’s persistence is expected to increase and the child’s distress is expected to decrease. In this way, the older sibling will serve as a surrogate attachment figure. These research findings have important implications for parenting behaviors as well as clinical practices.
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Brouwer, Kirk Rowse. "Enhancement of CFD Surrogate Approaches for Thermo-Structural Response Prediction in High-Speed Flows." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543340520905498.

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Sadet, Jérémy. "Surrogate models for the analysis of friction induced vibrations under uncertainty." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2022. http://www.theses.fr/2022UPHF0014.

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Le crissement automobile est une nuisance sonore qui intéresse à la fois les chercheurs et les industriels. Ce phénomène fugace, perçu par les acquéreurs de véhicule comme gage de piètre qualité, induit un coût de plus en plus important pour les équipementiers automobiles dû aux réclamations-client. Par conséquent, il est primordial de proposer et développer des méthodes permettant de prédire avec efficacité l’occurrence de cette nuisance sonore grâce à des modèles de simulation numérique. Ainsi, cette thèse se propose de poursuivre les récents travaux montrant l’apport certain d’une intégration des incertitudes au sein des simulations numériques de crissement. L’objectif est de proposer une stratégie de propagation d’incertitudes pour des simulations de crissement en maintenant des coûts numériques acceptables (en phase d’avant-projet). Plusieurs méthodes numériques sont évaluées et améliorées pour permettre des calculs à la fois précis et dans des temps de calcul compatibles avec les contraintes de l’industrie. Après avoir positionné ce travail de thèse par rapport aux avancées des chercheurs travaillant sur la thématique du crissement, une nouvelle méthode d’amélioration des solutions propres d’un problème aux valeurs propres complexes est proposée. Pour réduire les coûts numériques de telles études, trois modèles de substitution (processus gaussien, processus gaussien profond et réseau de neurones profonds) sont étudiés et comparés pour proposer les stratégies optimales que ce soit en termes de méthode ou de paramétrage. La construction de l’ensemble d’apprentissage est un élément clé pour assurer les futures prédictions des modèles de substitution. Une nouvelle stratégie/méthode d’optimisation exploitant l’optimisation bayésienne est présentée pour cibler idéalement le choix des données de l’ensemble d’apprentissage, données potentiellement coûteuses d’un point de vue numérique. Ces méthodes sont ensuite exploitées pour proposer une technique de propagation des incertitudes selon une modélisation par sous-ensembles flous
The automotive squeal is a noise disturbance, which has won the interest of the research and industrialists over the year. This elusive phenomenon, perceived by the vehicle purchasers as a poor-quality indicator, causes a cost which becomes more and more important for the car manufacturers, due to client’s claims. Thus, it is all the more important to propose and develop methods allowing predicting the occurring of this noise disturbance with efficiency, thanks to numerical simulations. Hence, this thesis proposes to pursue the recent works that showed the certain contributions of an integration of uncertainties into the squeal numerical simulations. The objective is to suggest a strategy of uncertainty propagation, for squeal simulations, maintaining numerical cost acceptable (especially, for pre-design phases). Several numerical methods are evaluated and improved to allow precise computations and with computational time compatible with the constraints of the industry. After positioning this thesis work with respect to the progress of the researchers working on the squeal subject, a new numerical method is proposed to improve the computation of the eigensolutions of a large quadratic eigenvalue problem. To reduce the numerical cost of such studies, three surrogate models (gaussian process, deep gaussian process and deep neural network) are studied and compared to suggest the optimal strategy in terms of methodology or model setting. The construction of the training set is a key aspect to insure the predictions of these surrogate models. A new optimisation strategy, hinging on bayesian optimisation, is proposed to efficiently target the samples of the training set, samples which are probably expensive to compute from a numerical point of view. These optimisation methods are then used to present a new uncertainty propagation method, relying on a fuzzy set modelisation
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Taheri, Mehdi. "Machine Learning from Computer Simulations with Applications in Rail Vehicle Dynamics and System Identification." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81417.

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The application of stochastic modeling for learning the behavior of multibody dynamics models is investigated. The stochastic modeling technique is also known as Kriging or random function approach. Post-processing data from a simulation run is used to train the stochastic model that estimates the relationship between model inputs, such as the suspension relative displacement and velocity, and the output, for example, sum of suspension forces. Computational efficiency of Multibody Dynamics (MBD) models can be improved by replacing their computationally-intensive subsystems with stochastic predictions. The stochastic modeling technique is able to learn the behavior of a physical system and integrate its behavior in MBS models, resulting in improved real-time simulations and reduced computational effort in models with repeated substructures (for example, modeling a train with a large number of rail vehicles). Since the sampling plan greatly influences the overall accuracy and efficiency of the stochastic predictions, various sampling plans are investigated, and a space-filling Latin Hypercube sampling plan based on the traveling salesman problem (TPS) is suggested for efficiently representing the entire parameter space. The simulation results confirm the expected increased modeling efficiency, although further research is needed for improving the accuracy of the predictions. The prediction accuracy is expected to improve through employing a sampling strategy that considers the discrete nature of the training data and uses infill criteria that considers the shape of the output function and detects sample spaces with high prediction errors. It is recommended that future efforts consider quantifying the computation efficiency of the proposed learning behavior by overcoming the inefficiencies associated with transferring data between multiple software packages, which proved to be a limiting factor in this study. These limitations can be overcome by using the user subroutine functionality of SIMPACK and adding the stochastic modeling technique to its force library.
Ph. D.
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Books on the topic "Surrogate dynamics"

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T, Patera Anthony, and Langley Research Center, eds. Surrogates for numerical simulations, optimization of eddy-promoter heat exchanges. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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Riches, D. Analysis and evaluation of different types of test surrogate employed in the dynamic performance testing of fall-arrest equipment. Sudbury: HSE Books, 2002.

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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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Majumdar, Anindita. Transnational Commercial Surrogacy and the (Un)Making of Kin in India. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199474363.001.0001.

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Billed as an emerging transnational industry, the commercial surrogacy arrangement is more than mere commerce. It involves the birth of kin and relationships that include cross-cultural dialogues and conflicts between forms of reproduction and birthing. The process of making kin is fraught with different forms of negotiations regarding biology, nurture, pregnancy, and parenthood. This book engages with the idea of emerging forms of families and meanings of kinship in a transnational world through ethnographic research, kinship, gender studies, and science and technology studies. The ethnography draws from a context that is enmeshed in the local–global politics of reproduction, and the engaging and ongoing debate regarding ethics and morality in the sphere of reproductive rights. Drawing from conversations with foreign couples coming to India to hire Indian surrogates through Indian fertility clinics, lawmakers, and clinicians, this book looks at the politics of foreign gay couples seeking families through surrogacy in India, identity giving processes to the babies born to foreign couples, the clinicians understanding of kinship, the networks of commerce and agents, and the ways in which the surrogate and her husband positions itself within the arrangement. The mapping of transnational commercial surrogacy in its processual, dynamic representation—from the choice of the arrangement, to the pregnancy and finally to the birth of the child—is done in broad stages. This book aims to present an important ethnographic picture of a complicated, controversial practice such as commercial surrogacy by focusing on its relevance for kinship and our understanding of interpersonal relationships at large.
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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

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Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test this null hypothesis using surrogate data testing. The combination of a noticeably regular shadow attractor, along with strong statistical rejection of fortuitous regularity, increases the probability that observed data are generated by deterministic real-world dynamics.
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McAuley, Danny F., and Thelma Rose Craig. Measurement of extravascular lung water in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0140.

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The accumulation of fluid in the interstitium and alveolar space is known as extravascular lung water (EVLW). EVLW is associated with increased morbidity and mortality in critically ill patients and is elevated in patients with cardiogenic pulmonary oedema, acute lung injury (ALI), and the acute respiratory distress syndrome (ARDS). Pulmonary oedema is a consequence of increased pulmonary capillary hydrostatic pressure and/or an increased capillary permeability. The quantity of pulmonary oedema fluid is dependent on the balance of fluid formation and clearance, and this contributes to the overall dynamic net lung fluid balance. Measurement of EVLW is therefore an indirect surrogate measurement of the alveolar epithelial and endothelial damage in ALI/ARDS. The single indicator transpulmonary thermodilution technique is an available bedside technique to measure EVLW.
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Book chapters on the topic "Surrogate dynamics"

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Husain, Afzal, and Kwang-Yong Kim. "Optimization of Ribbed Microchannel Heat Sink Using Surrogate Analysis." In Computational Fluid Dynamics 2008, 529–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01273-0_69.

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Paripovic, Jelena, and Patricia Davies. "Characterizing the Dynamics of Systems Incorporating Surrogate Energetic Materials." In Special Topics in Structural Dynamics, Volume 6, 101–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29910-5_10.

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Chiambaretto, Pierre-Louis, Miguel Charlotte, Joseph Morlier, Philippe Villedieu, and Yves Gourinat. "Surrogate Granular Materials for Modal Test of Fluid Filled Tanks." In Special Topics in Structural Dynamics, Volume 6, 139–46. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29910-5_14.

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Taflanidis, Alexandros A., Jize Zhang, and Dimitris Patsialis. "Applications of Reduced Order and Surrogate Modeling in Structural Dynamics." In Model Validation and Uncertainty Quantification, Volume 3, 297–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12075-7_35.

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Kůdela, Jakub, and Ladislav Dobrovský. "Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems." In Lecture Notes in Computer Science, 303–21. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70068-2_19.

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Sipponen, P., O. Suovaniemi, and M. Härkönen. "The role of pepsinogen assays as surrogate markers of gastritis dynamics in population studies." In Helicobactor pylori, 127–32. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-1763-2_12.

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Denimal, E., L. Nechak, J. J. Sinou, and S. Nacivet. "A New Surrogate Modeling Method Associating Generalized Polynomial Chaos Expansion and Kriging for Mechanical Systems Subjected to Friction-Induced Vibration." In Special Topics in Structural Dynamics, Volume 6, 17–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53841-9_2.

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Morales, Xabier, Jordi Mill, Kristine A. Juhl, Andy Olivares, Guillermo Jimenez-Perez, Rasmus R. Paulsen, and Oscar Camara. "Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage." In Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 157–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39074-7_17.

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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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Smith, Jonathan R. "Surrogate Aerodynamics Modeling Applied to Surrogate Structural Dynamical Systems." In Model Validation and Uncertainty Quantification, Volume 3, 189–92. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37003-8_30.

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Conference papers on the topic "Surrogate dynamics"

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LENGEL, RUSSELL, and JEFFREY LINDER. "The use of rubidium as a surrogate for potassium in combustion system imaging." In 21st Fluid Dynamics, Plasma Dynamics and Lasers Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-1547.

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Boopathy, Komahan, and Markus P. Rumpfkeil. "A Multivariate Interpolation and Regression Enhanced Kriging Surrogate Model." In 21st AIAA Computational Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2964.

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Choze, Sergio, and Felipe A. Viana. "Simple and inexpensive algorithm for surrogate filtering." In 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-0139.

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Leifsson, Leifur, and Slawomir Koziel. "Surrogate-Based Shape Optimization of Low-Speed Wind Tunnel Contractions." In 42nd AIAA Fluid Dynamics Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-3344.

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Beyhaghi, Pooriya, Daniele Cavaglieri, and Thomas Bewley. "Delaunay-based Derivative-free Optimization via Global Surrogate, Part 1: Theory." In 21st AIAA Computational Fluid Dynamics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2707.

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Park, Chanyoung, Raphael T. Haftka, and Nam Ho Kim. "Simple Alternative to Bayesian Multi-Fidelity Surrogate Framework." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0135.

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Chowdhury, Souma, Ali Mehmani, Weiyang Tong, and Achille Messac. "Adaptive Model Refinement in Surrogate-based Multiobjective Optimization." In 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-0417.

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Trizila, Patrick, Chang-Kwon Kang, Miguel Visbal, and Wei Shyy. "Unsteady Fluid Physics and Surrogate Modeling of Low Reynolds Number, Flapping Airfoils." In 38th Fluid Dynamics Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-3821.

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Pyle, James, Mozhgan Kabiri Chimeh, and Paul Richmond. "Surrogate Modelling for Efficient Discovery of Emergent Population Dynamics." In 2019 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2019. http://dx.doi.org/10.1109/hpcs48598.2019.9188208.

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Viana, Felipe, and Raphael Haftka. "Importing Uncertainty Estimates from One Surrogate to Another." In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-2237.

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Reports on the topic "Surrogate dynamics"

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Meidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.

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Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.
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Elliott, J. Hydra modeling of experiments to study ICF capsule fill hole dynamics using surrogate targets. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/925990.

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Torres, Marissa, Michael-Angelo Lam, and Matt Malej. Practical guidance for numerical modeling in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45641.

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This technical note describes the physical and numerical considerations for developing an idealized numerical wave-structure interaction modeling study using the fully nonlinear, phase-resolving Boussinesq-type wave model, FUNWAVE-TVD (Shi et al. 2012). The focus of the study is on the range of validity of input wave characteristics and the appropriate numerical domain properties when inserting partially submerged, impermeable (i.e., fully reflective) coastal structures in the domain. These structures include typical designs for breakwaters, groins, jetties, dikes, and levees. In addition to presenting general numerical modeling best practices for FUNWAVE-TVD, the influence of nonlinear wave-wave interactions on regular wave propagation in the numerical domain is discussed. The scope of coastal structures considered in this document is restricted to a single partially submerged, impermeable breakwater, but the setup and the results can be extended to other similar structures without a loss of generality. The intended audience for these materials is novice to intermediate users of the FUNWAVE-TVD wave model, specifically those seeking to implement coastal structures in a numerical domain or to investigate basic wave-structure interaction responses in a surrogate model prior to considering a full-fledged 3-D Navier-Stokes Computational Fluid Dynamics (CFD) model. From this document, users will gain a fundamental understanding of practical modeling guidelines that will flatten the learning curve of the model and enhance the final product of a wave modeling study. Providing coastal planners and engineers with ease of model access and usability guidance will facilitate rapid screening of design alternatives for efficient and effective decision-making under environmental uncertainty.
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Harris and Edlund. L51766 Instantaneous Rotational Velocity Development. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 1997. http://dx.doi.org/10.55274/r0010119.

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Considerable effort has been put forth to develop an automated method for balancing the power cylinders of reciprocating integral engines used in the natural gas industry. The benefits to power cylinder balance include reducted emissions and improved cylinder component mechanical integrity (which should lead to reductions in repair costs). The current approach to automate engine balancing uses pressure transducers to measure cylinder pressure, then integrate the signals into the engine fuel /timing management controller to achieve engine balance. Each power cylinder must be instrumented, which quickly leads to an expensive installation package. For large units (12 to 16 power cylinders), the likelihood of transducer failure and / or calibration changes will be problematic to reliable operation of this autobalancing system. A potential alternative to multiple transducers measuring power cylinder pressure is to use a single transducer to measure instantaneous shaft rotational velocity. Instantaneous shaft rotational velocity is driven by engine / compressor torque loads, and therefore is sensitive to changes in both power and compressor cylinder operation. This report summarizes the results of an investigation into the possible use of the flywheel rotational velocity as a surrogate for power cylinder pressure measurements in an autobalancing arrangement, or as a balanced/need-to-balance indicator for integral engines. A fundamental model of the rotational kinetics/dynamics was developed and used to predict the flywheel rotational acceleration. The model was validated and enhanced with data acquired as part of this study. The model was then extended to establish a sensitivity matrix, which established the change in predicted torque as a function of power cylinder imbalance. Using the sensitivity matrix, an algorithm was developed to predict the change in power cylinder peak pressures as a function of the change in the measured shaft rotational velocity.
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.
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