Computational Mechanics & Artificial Intelligence Lab
Nick Napoleon Vlassis is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Rutgers University.
Home
About
Publications
Google Scholar
Hiring
News Feed
Geometric Deep Learning for Computational Mechanics Part I: Anisotropic Hyperelasticity
Geometric Deep Learning for Computational Mechanics Part II: Graph Embedding for Interpretable Multiscale Plasticity
Sobolev Training of Thermodynamic-Informed Neural Networks for Interpretable Elasto-Plasticity Models with Level Set Hardening
Molecular Dynamics Inferred Transfer Learning Models for Finite-Strain Hyperelasticity of Monoclinic Crystals: Sobolev Training and Validations Against Physical Constraints
Component-Based Machine Learning Paradigm for Discovering Rate-Dependent and Pressure-Sensitive Level-Set Plasticity Models
Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties
nick.vlassis@rutgers.edu
Sobolev Training of Thermodynamic-Informed Neural Networks for Interpretable Elasto-Plasticity Models with Level Set Hardening
Vlassis, Sun, CMAM, 2021.
We introduce a deep learning framework to train smoothed elastoplasticity models with interpretable components like stored elastic energy function, yield surface, and plastic flow that evolve based on deep neural network predictions. We use the Hamilton-Jacobi equation to deduce solutions for the hardening/softening mechanism and Sobolev training to ensure thermodynamic consistency and interpretability. Our numerical experiments using data from a 3D FFT solver show that this approach provides more accurate forward predictions of cyclic stress paths than other black-box models such as recurrent neural network, 1D convolutional neural network, and multi-step feed-forward model.
Read more →
Read more →
