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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 ...
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 ...