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RISExtending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Design optimization under uncertainty is notoriously difficult when the objective function is expensive to evaluate. State-of-the-art techniques, e.g., stochastic optimization or sampling average approximation, fail to ...
Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: Bayesian optimal design of experiments (BODEs) have been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the ...
Scalable Fully Bayesian Gaussian Process Modeling and Calibration With Adaptive Sequential Monte Carlo for Industrial Applications
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Gaussian process (GP) regression or kriging has been extensively applied in the engineering literature for the purposes of building a cheap-to-evaluate surrogate, within the contexts of multi-fidelity modeling, model ...
Efficient Mapping Between Void Shapes and Stress Fields Using Deep Convolutional Neural Networks With Sparse Data
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often ...
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical response. This problem is aggravated by limited ...
Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional ...