Relevant bibliographies by topics / Bayesian optimization technique

Academic literature on the topic 'Bayesian optimization technique'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Bayesian optimization technique.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Bayesian optimization technique"

1

He, Xiang Dong, Jun Yan Huang, and Shu Tian Liu. "Bayesian Reliability-Based Optimization Design of Torsion Bar." Advanced Materials Research 538-541 (June 2012): 3085–88. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.3085.

Full text
Abstract:
Based on bayesian statistics theory and reliability-based optimization design, the research presents a new reliability-based optimization design approach that solves the form of finite test samples. In the article, the bayesian reliability-based optimization mathematical model is established and the bayesian reliability-based optimization approach of torsion bar is proposed. The method adopts a bayesian inference technique to estimate reliability, gives definition of bayesian reliability. The results illustrates the method presented is an efficient and practical reliability-based optimization approach of torsion bar.
APA, Harvard, Vancouver, ISO, and other styles
2

Lockhart, Brandon, Jinglin Peng, Weiyuan Wu, Jiannan Wang, and Eugene Wu. "Explaining inference queries with bayesian optimization." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2576–85. http://dx.doi.org/10.14778/3476249.3476304.

Full text
Abstract:
Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks to explain unexpected aggregate query results on inference data; such queries are challenging to explain because an explanation may need to be derived from the source, training, or inference data in an ML pipeline. In this paper, we model an objective function as a black-box function and propose BOExplain, a novel framework for explaining inference queries using Bayesian optimization (BO). An explanation is a predicate defining the input tuples that should be removed so that the query result of interest is significantly affected. BO --- a technique for finding the global optimum of a black-box function --- is used to find the best predicate. We develop two new techniques (individual contribution encoding and warm start) to handle categorical variables. We perform experiments showing that the predicates found by BOExplain have a higher degree of explanation compared to those found by the state-of-the-art query explanation engines. We also show that BOExplain is effective at deriving explanations for inference queries from source and training data on a variety of real-world datasets. BOExplain is open-sourced as a Python package at https://github.com/sfu-db/BOExplain.
APA, Harvard, Vancouver, ISO, and other styles
3

Coles, Darrell, and Andrew Curtis. "Efficient nonlinear Bayesian survey design using DN optimization." GEOPHYSICS 76, no. 2 (March 2011): Q1—Q8. http://dx.doi.org/10.1190/1.3552645.

Full text
Abstract:
A new method for fully nonlinear, Bayesian survey design renders the optimization of industrial-scale geoscientific surveys as a practical possibility. The method, DN optimization, designs surveys to maximally discriminate between different possible models. It is based on a generalization to nonlinear design problems of the D criterion (which is for linearized design problems). The main practical advantage of DN optimization is that it uses efficient algorithms developed originally for linearized design theory, resulting in lower computing and storage costs than for other nonlinear Bayesian design techniques. In a real example in which we optimized a seafloor microseismic sensor network to monitor a fractured petroleum reservoir, we compared DN optimization with two other networks: one proposed by an industrial contractor and one optimized using a linearized Bayesian design method. Our technique yielded a network with superior expected data quality in terms of reduced uncertainties on hypocenter locations.
APA, Harvard, Vancouver, ISO, and other styles
4

Vendrov, Ivan, Tyler Lu, Qingqing Huang, and Craig Boutilier. "Gradient-Based Optimization for Bayesian Preference Elicitation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10292–301. http://dx.doi.org/10.1609/aaai.v34i06.6592.

Full text
Abstract:
Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning computational frameworks (e.g., TensorFlow, PyTorch). We exploit this to develop a novel Monte Carlo method for EVOI optimization, which is much more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or “partial items,” which are often more cognitively manageable for users. Experiments show that our gradient-based EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.
APA, Harvard, Vancouver, ISO, and other styles
5

Sun, Xingping, Chang Chen, Lu Wang, Hongwei Kang, Yong Shen, and Qingyi Chen. "Hybrid Optimization Algorithm for Bayesian Network Structure Learning." Information 10, no. 10 (September 24, 2019): 294. http://dx.doi.org/10.3390/info10100294.

Full text
Abstract:
Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.
APA, Harvard, Vancouver, ISO, and other styles
6

SATO, Wataru, Koma SATO, Nobuyuki ISOSHIMA, Yoko MAKINO, and Masashi SHIBAHARA. "Development of Optimizing Technique for Temperature Control Based on Bayesian Optimization." Proceedings of Mechanical Engineering Congress, Japan 2021 (2021): J122–10. http://dx.doi.org/10.1299/jsmemecj.2021.j122-10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

OKIKIOLA, F. M., O. S. ADEWALE, A. M. MUSTAPHA, A. M. IKOTUN, and O. L. LAWAL. "A FRAMEWORK FOR ONTOLOGY- BASED DIABETES DIAGNOSIS USING BAYELSIAN OPTIMIZATION TECHNIQUE." Journal of Natural Sciences Engineering and Technology 17, no. 1 (November 6, 2019): 156–68. http://dx.doi.org/10.51406/jnset.v17i1.1906.

Full text
Abstract:
Diabetes Management System (DMS) is a computer-based system which aid physicians in properly diagnosing diabetes mellitus disease in patients. The DMS is essential in making individuals who have diabetes aware of their state and type. Existing approaches employed have not been efficient in considering all the diabetes type as well as making full prescription to diabetes patients. In this paper, a framework for an improved Ontology-based Diabetes Management System with a Bayesian optimization technique is presented. This helped in managing the diagnosis of diabetes and the prescription of treatment and drug to patients using the ontology knowledge management. The framework was implemented using Java programming language on Netbeans IDE, Protégé 4.2 and mysql. An extract of the ontology graph and acyclic probability graph was shown. The result showed that the nature of Bayesian network which has to do with statistical calculations based on equations, functions and sample frequencies led to more precise and reliable outcome.
APA, Harvard, Vancouver, ISO, and other styles
8

Hu, Yumei, Xuezhi Wang, Hua Lan, Zengfu Wang, Bill Moran, and Quan Pan. "An Iterative Nonlinear Filter Using Variational Bayesian Optimization." Sensors 18, no. 12 (December 1, 2018): 4222. http://dx.doi.org/10.3390/s18124222.

Full text
Abstract:
We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.
APA, Harvard, Vancouver, ISO, and other styles
9

Liu, Bin, and Chun Lin Ji. "Automated Metamaterial Design with Computer Model Emulation and Bayesian Optimization." Applied Mechanics and Materials 575 (June 2014): 201–5. http://dx.doi.org/10.4028/www.scientific.net/amm.575.201.

Full text
Abstract:
We present an automated computation system for large scale design of metamaterials (MTMs). A computer model emulation (CME) technique is used to generate a forward mapping from the MTM particle’s geometric dimension to the corresponding electromagnetic (EM) response. Then the design problem translates to be a reverse engineering process which aims to find optimal values of the geometric dimensions for the MTM particles. The core of the CME process is a statistical functional regression module using a Gaussian Process mixture (GPM) model. The reverse engineering process is implemented with a Bayesian optimization technique. Experimental results demonstrate that the proposed approach can facilitate rapid design of MTMs.
APA, Harvard, Vancouver, ISO, and other styles
10

Lavielle, Marc. "2-D Bayesian deconvolution." GEOPHYSICS 56, no. 12 (December 1991): 2008–18. http://dx.doi.org/10.1190/1.1443013.

Full text
Abstract:
Inverse problems can be solved in different ways. One way is to define natural criteria of good recovery and build an objective function to be minimized. If, instead, we prefer a Bayesian approach, inversion can be formulated as an estimation problem where a priori information is introduced and the a posteriori distribution of the unobserved variables is maximized. When this distribution is a Gibbs distribution, these two methods are equivalent. Furthermore, global optimization of the objective function can be performed with a Monte Carlo technique, in spite of the presence of numerous local minima. Application to multitrace deconvolution is proposed. In traditional 1-D deconvolution, a set of uni‐dimensional processes models the seismic data, while a Markov random field is used for 2-D deconvolution. In fact, the introduction of a neighborhood system permits one to model the layer structure that exists in the earth and to obtain solutions that present lateral coherency. Moreover, optimization of an appropriated objective function by simulated annealing allows one to control the fit with the input data as well as the spatial distribution of the reflectors. Extension to 3-D deconvolution is straightforward.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Bayesian optimization technique"

1

Rawat, Waseem. "Optimization of convolutional neural networks for image classification using genetic algorithms and bayesian optimization." Diss., 2018. http://hdl.handle.net/10500/24977.

Full text
Abstract:
Notwithstanding the recent successes of deep convolutional neural networks for classification tasks, they are sensitive to the selection of their hyperparameters, which impose an exponentially large search space on modern convolutional models. Traditional hyperparameter selection methods include manual, grid, or random search, but these require expert knowledge or are computationally burdensome. Divergently, Bayesian optimization and evolutionary inspired techniques have surfaced as viable alternatives to the hyperparameter problem. Thus, an alternative hybrid approach that combines the advantages of these techniques is proposed. Specifically, the search space is partitioned into discrete-architectural, and continuous and categorical hyperparameter subspaces, which are respectively traversed by a stochastic genetic search, followed by a genetic-Bayesian search. Simulations on a prominent image classification task reveal that the proposed method results in an overall classification accuracy improvement of 0.87% over unoptimized baselines, and a greater than 97% reduction in computational costs compared to a commonly employed brute force approach.
Electrical and Mining Engineering
M. Tech. (Electrical Engineering)
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Bayesian optimization technique"

1

Abkar, Ali Akbar. Likelihood-based segmentation and classification of remotely sensed images: A Bayesian optimization approach for combining RS and GIS. Enschede, The Netherlands: International Institute for Aerospace Survey and Earth Sciences, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Bayesian optimization technique"

1

Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning for Hyperparameter Optimization." In Metalearning, 103–22. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_6.

Full text
Abstract:
SummaryThis chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses Bayesian optimization, a technique that learns from the observed performance of previously tried hyperparameter settings on the current task. This knowledge is used to build a meta-model (surrogate model) that can be used to predict which unseen configurations may work better on that task. This part includes the description sequential model-based optimization (SMBO). This chapter also covers metalearning techniques that extend the previously discussed optimization techniques with the ability to transfer knowledge across tasks. This includes techniques such as warm-starting the search, or transferring previously learned meta-models that were trained on prior (similar) tasks. A key question here is how to establish how similar prior tasks are to the new task. This can be done on the basis of past experiments, but can also exploit the information gained from recent experiments on the target task. This chapter presents an overview of some recent methods proposed in this area.
APA, Harvard, Vancouver, ISO, and other styles
2

Val, Anabel del, Olivier P. Le Maître, Olivier Chazot, Pietro M. Congedo, and Thierry E. Magin. "Inference Methods for Gas-Surface Interaction Models: From Deterministic Approaches to Bayesian Techniques." In Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications, 349–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80542-5_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gregori, S., M. Tur, A. Pedrosa, and F. J. Fuenmayor. "The Use of Bayesian Optimisation Techniques for the Pantograph-Catenary Dynamic Interaction Stochastic Problem." In EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization, 997–1008. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97773-7_86.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Al-Khasawneh, Ahmad. "A Method for Classification Using Data Mining Technique for Diabetes." In Virtual and Mobile Healthcare, 127–50. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9863-3.ch006.

Full text
Abstract:
Many researchers in the health information system field have been attracted to develop computer applications that help in the diagnosis process. Imperatively, data mining algorithms address the vital role in all of these applications. Many contributions were made in this area. There has always been a debate on the algorithm that gives the best classifier, the parameters to be used, the dataset pre-processing steps, etc. In this paper, the author largely emphasizes that the best way to build a predictive model with relatively high classification accuracy is to build several predictive models and to choose the model that gives the best results through parameters optimization. Diagnosing diabetes mellitus has gained considerable attention in the last few decades due to the increased severity of the disease. In this research, the author reviews four predictive data mining approaches that are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset; k-nearest neighbour, support vector machine, multilayer perceptron neural network, and naive bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.
APA, Harvard, Vancouver, ISO, and other styles
5

Nechval, N. A., and K. N. Nechval. "Effective Optimization of Statistical Decisions for Age Replacement Problems under Parametric Uncertainty." In Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, 1–16. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1639-2.ch001.

Full text
Abstract:
In this chapter, an innovative model for age replacement is proposed. The costs included in the age replacement model are not assumed to be constants. For effective optimization of statistical decisions for age replacement problems under parametric uncertainty, based on a past random sample of lifetimes, the pivotal quantity averaging (PQA) approach is suggested. The PQA approach represents a simple and computationally attractive statistical technique. In this case, the transition from the original problem to the equivalent transformed problem (in terms of pivotal quantities and ancillary factors) is carried out via invariant embedding a sample statistic in the original problem. The approach allows one to eliminate unknown parameters from the problem and to find the better decision rules, which have smaller risk than any of the well-known decision rules. Unlike the Bayesian approach, the proposed approach is independent of the choice of priors. For illustration, numerical examples are given.
APA, Harvard, Vancouver, ISO, and other styles
6

"Empirical Bayes Process Monitoring Techniques." In Bayesian Process Monitoring, Control and Optimization, 121–50. Chapman and Hall/CRC, 2006. http://dx.doi.org/10.1201/9781420010701-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Horng Shiau, Jyh-Jen, and Carol Feltz. "Empirical Bayes Process Monitoring Techniques." In Bayesian Process Monitoring, Control and Optimization, 109–38. Chapman and Hall/CRC, 2006. http://dx.doi.org/10.1201/9781420010701.ch4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

"Advanced SLAM Techniques." In Simultaneous Localization and Mapping for Mobile Robots, 336–89. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2104-6.ch010.

Full text
Abstract:
This chapter is the conclusion of the book. It is devoted to providing an overview of emerging paradigms that are appearing as outstanding the traditional approaches in scalability or efficiency, such as hierarchical sub-mapping, or hybrid metric-topological map models. Other techniques not based on Bayesian filtering, such as iterative sparse least-squares optimization (Graph-SLAM and Bundle adjustment), are also introduced due to their efficiency and increasing popularity.
APA, Harvard, Vancouver, ISO, and other styles
9

Loskot, Pavel. "Bayesian Methods and Monte Carlo Simulations." In Numerical Simulation [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108699.

Full text
Abstract:
Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear stochastic systems. They allow tracking changes in probability distributions by applying Bayes’s theorem and the chain rule for factoring the probabilities. However, an excessive complexity of resulting distributions often dictates the use of numerical methods when performing statistical and causal inferences over probabilistic models. In this chapter, the Bayesian methods for intractable distributions are first introduced as sampling, filtering, approximation, and likelihood-free methods. Their fundamental principles are explained, and the key challenges are identified. The concise survey of Bayesian methods is followed by outlining their applications. In particular, Bayesian experiment design aims at maximizing information gain or utility, and it is often combined with an optimum model selection. Bayesian hypothesis testing introduces optimality in the data-driven decision making. Bayesian machine learning assumes data labels to be random variables. Bayesian optimization is a powerful strategy for configuring and optimizing large-scale complex systems, for which conventional optimization techniques are usually ineffective. The chapter is concluded by examining Bayesian Monte Carlo simulations. It is proposed that augmented Monte Carlo simulations can achieve explainability and also provide much better information efficiency.
APA, Harvard, Vancouver, ISO, and other styles
10

Mansouri, Majdi, Hazem Numan Nounou, and Mohamed Numan Nounou. "Modeling and Monitoring of Chemical System." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, 835–58. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch032.

Full text
Abstract:
This chapter addresses the problem of time-varying nonlinear modeling and monitoring of a continuously stirred tank reactor (CSTR) process using state estimation techniques. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently the variational Bayesian filter (VBF). The objectives of this chapter are threefold. The first objective is to use the variational Bayesian filter with better proposal distribution for nonlinear states and parameters estimation. The second objective is to extend the state and parameter estimation techniques to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. The third objective is to apply the state estimation techniques EKF, PF and VBF for time-varying nonlinear modeling and monitoring of CSTR process. The estimation performance is evaluated on a synthetic example in terms of estimation accuracy, root mean square error and execution times.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Bayesian optimization technique"

1

Xu, Liuyang, Zhongyue Zhang, Zhengyang Li, and Juntao Ke. "Acquisition Function Selection: Bayesian Optimization in Neural Network Technique." In ICBDR 2020: 2020 the 4th International Conference on Big Data Research. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3445945.3445957.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Sharpe, Conner, Carolyn Conner Seepersad, Seth Watts, and Dan Tortorelli. "Design of Mechanical Metamaterials via Constrained Bayesian Optimization." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85270.

Full text
Abstract:
Advances in additive manufacturing processes have made it possible to build mechanical metamaterials with bulk properties that exceed those of naturally occurring materials. One class of these metamaterials is structural lattices that can achieve high stiffness to weight ratios. Recent work on geometric projection approaches has introduced the possibility of optimizing these architected lattice designs in a drastically reduced parameter space. The reduced number of design variables enables application of a new class of methods for exploring the design space. This work investigates the use of Bayesian optimization, a technique for global optimization of expensive non-convex objective functions through surrogate modeling. We utilize formulations for implementing probabilistic constraints in Bayesian optimization to aid convergence in this highly constrained engineering problem, and demonstrate results with a variety of stiff lightweight lattice designs.
APA, Harvard, Vancouver, ISO, and other styles
3

Iyer, Akshay, Suraj Yerramilli, James M. Rondinelli, Daniel W. Apley, and Wei Chen. "Descriptor Aided Bayesian Optimization for Mixed Variable Materials Design With High Dimensional Qualitative Variables." 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/detc2022-90177.

Full text
Abstract:
Abstract Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those encountered in materials design, underline the need for efficient search strategies — Bayesian optimization being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors to infer the effect of unobserved levels that have not been sampled yet. We show that domain knowledge about physical descriptors can be intuitively embedded into the latent variable Gaussian process approach — a mixed-variable GP modeling technique — and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. Our method is robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.
APA, Harvard, Vancouver, ISO, and other styles
4

Iyer, Akshay, Yichi Zhang, Aditya Prasad, Siyu Tao, Yixing Wang, Linda Schadler, L. Catherine Brinson, and Wei Chen. "Data-Centric Mixed-Variable Bayesian Optimization for Materials Design." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98222.

Full text
Abstract:
Abstract Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify “lack of data” uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.
APA, Harvard, Vancouver, ISO, and other styles
5

Gunawan, Subroto, and Panos Y. Papalambros. "A Bayesian Approach to Reliability-Based Optimization With Incomplete Information." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99458.

Full text
Abstract:
In engineering design, information regarding the uncertain variables or parameters is usually in the form of finite samples. Existing methods in optimal design under uncertainty cannot handle this form of incomplete information; they have to either discard some valuable information or postulate existence of additional information. In this article, we present a reliability-based optimization method that is applicable when information of the uncertain variables or parameters is in the form of both finite samples and probability distributions. The method adopts a Bayesian Binomial inference technique to estimate reliability, and uses this estimate to maximize the confidence that the design will meet or exceed a target reliability. The method produces a set of Pareto trade-off designs instead of a single design, reflecting the levels of confidence about a design’s reliability given certain incomplete information. As a demonstration, we apply the method to design an optimal piston-ring/cylinder-liner assembly under surface roughness uncertainty.
APA, Harvard, Vancouver, ISO, and other styles
6

Baheri, Ali, Praveen Ramaprabhu, and Christopher Vermillion. "Iterative In-Situ 3D Layout Optimization of a Reconfigurable Ocean Current Turbine Array Using Bayesian Optimization." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5230.

Full text
Abstract:
In this paper, we present an online approach for optimizing the 3D layout of an ocean current turbine (OCT) array. Unlike towered turbines, most OCT concepts for Gulf Stream energy harvesting involve tethered systems. The replacement of towers with tethers provides the opportunity for OCTs to adjust their locations within some domain by paying out/in tether to adjust depth and manipulating control surfaces (elevators and rudders) to adjust longitudinal and lateral positions. The ability to adjust the OCT positions online provides the capacity to reconfigure the array layout in response to changing flow conditions; however, successful online array layout reconfiguration requires optimization schemes that are not only effective but also enable fast convergence to the optimal configuration. To address the above needs, we present a reconfigurable layout optimization algorithm with two novel features. First, we describe the location of each turbine through a small set of basis parameters; the number of basis parameters does not grow with increasing array size, thereby leading to an optimization that is not only computationally tractable but is also highly scalable. Secondly, we use Bayesian Optimization to optimize these basis parameters. Bayesian Optimization is a very powerful iterative optimization technique that, at every iteration, fuses a best-guess model of a complex function (array power as a function of basis parameters, in our case) with a characterization of the model uncertainty in order to determine the next evaluation point. Using a low-order analytical wake interaction model, we demonstrate the effectiveness of the proposed optimization approach for various array sizes.
APA, Harvard, Vancouver, ISO, and other styles
7

Kaya, Mine, and Shima Hajimirza. "Using Bayesian Optimization With Knowledge Transfer for High Computational Cost Design: A Case Study in Photovoltaics." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-98111.

Full text
Abstract:
Abstract Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.
APA, Harvard, Vancouver, ISO, and other styles
8

Baheri, Ali, Joseph Deese, and Christopher Vermillion. "Combined Plant and Controller Design Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5242.

Full text
Abstract:
This paper presents a novel data-driven nested optimization framework that aims to solve the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamics is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve the nested optimization problem. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent control or plant parameters. The proposed framework differs from the majority of co-design literature where there exists a closed-form model of the system dynamics. We validate the proposed framework for Altaeros’ Buoyant Airborne Turbine (BAT). We choose the horizontal stabilizer area and longitudinal center of mass relative to center of buoyancy (plant parameters) and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that plant and control parameters converge to optimal values within only a few iterations.
APA, Harvard, Vancouver, ISO, and other styles
9

Serafino, Aldo, Benoit Obert, Hayato Hagi, and Paola Cinnella. "Assessment of an Innovative Technique for the Robust Optimization of Organic Rankine Cycles." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-90170.

Full text
Abstract:
Abstract After the extraordinary diffusion that we have observed over the last ten years, Organic Rankine Cycles (ORCs) are nowadays widely recognized as “the unrivalled technical solution for generating electricity from low-medium temperature heat sources of limited capacity” [1]. Despite the high level of confidence and know-how reached about ORCs, they still remain a delicate technology, hiding a great amount of technical difficulties which sometimes still make them a risky investment. Most of these complexities are originated from manifold sources of uncertainty which impact on almost the whole life of the ORC project, from their design to the commissioning and operation steps, with heavy consequences in terms of performance and costs. In this work we present the proof of concept assessing and validating an innovative technique for the robust design optimization (RDO) of ORC under uncertainty. The approach allows to deal with both aleatory and epistemic uncertainty in order to avoid an over-optimization of the system that can result in a high sensitivity to small changes. Because of the large number of sources of uncertainty, the design problem must be solved in a highly multi-dimensional space, spanned by the uncertain and design variables. In such a situation, the “brute-force” Monte-carlo approach [2] is not a viable technique, since it is limited to cheap and excessively simplified models. Consequently, in the present work we consider a more efficient design methodology relying on two nested Bayesian Kriging surrogates.
APA, Harvard, Vancouver, ISO, and other styles
10

Chandrashekar, Natesh, and Sundar Krishnamurty. "Bayesian Evaluation of Engineering Models." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34141.

Full text
Abstract:
This paper deals with the development of simulation-based design models under uncertainty, and presents an approach for building surrogate models and validating them for their efficacy and relevance from a design decision perspective. Specifically, this work addresses the fundamental research issue of how to build such surrogate models that are computationally efficient and sufficiently accurate, and meaningful from the viewpoint of its subsequent use in design. Towards this goal, this work presents a Bayesian analysis based iterative model building and model validation process leading to reliable and accurate surrogate models, which can then be invoked in the final design optimization phase. The resulting surrogate models can be expected to act as abstractions or idealizations of the engineering analysis models and can mimic system performance in a computationally efficient manner to facilitate design decisions under uncertainty. This is accomplished by first building initial models, and then refining and validating them over many stages, in line with the iterative nature of the engineering design process. Salient features of this work include the introduction of a novel preference-based design screening strategy nested in an optimally-selected prior information set for validation purposes; and the use of a Bayesian evaluation based model-updating technique to capture new information and enhance model’s value and effectiveness. A case study of the design of a windshield wiper arm is used to demonstrate the overall methodology and the results are discussed.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Bayesian optimization technique' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography