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Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials by Bharadwaja, B V S S; Nabian, Mohammad Amin; Sharma, Bharatkumar; Choudhry, Sanjay; Alankar, Alankar is a scholarly article available to read on EtoBox.

What is Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials about?

In this work, a model based on the Physics - Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ) is presented. For the present study, the PINNs framework - Modulus developed by Nvidia is utilized, wherein we implement a module for mechanics of heterogeneous solids. We use PINNs to approximate momentum balance by assuming isotropic linear elastic constitutive behavior against a loss function. Along with governing equations, the associated initial / boundary conditions also softly participate in the loss function. Solids where the heterogeneity manifests as voids (low elastic modulus regions) and fibers (high elastic modulus regions) in a matrix are analyzed, and the results are validated against solutions obtained from a commercial Finite Element (FE) analysis package. The present study also reveals that PINNs can capture the stress jumps precisely at the material interfaces. Additionally, the present study explores the advantages associated with the surrogate features in PINNs via the variation in geometry and material properties. The presented UQ studies suggest that the mean and standard deviation

Author

Bharadwaja, B V S S; Nabian, Mohammad Amin; Sharma, Bharatkumar; Choudhry, Sanjay; Alankar, Alankar

Published

2022

Language

EN