Computational Mechanics & Artificial Intelligence Lab
Nick Napoleon Vlassis is an Assistant Professor in the Department of Mechanical & Aerospace Engineering at Rutgers University.
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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
Geometric Deep Learning for Computational Mechanics Part II: Graph Embedding for Interpretable Multiscale Plasticity
Vlassis, Sun, CMAME, 2022.
We use geometric deep learning on graph data representations of finite element solutions to connect nonlinear dimensional reduction techniques with plasticity models. We use geometric learning-based encoding on graphs to embed rich time-history data onto a low-dimensional Euclidean space to predict the evolution of plastic deformation, and a corresponding decoder converts these low-dimensional internal variables back into a weighted graph to observe and analyze the dominating topological features of plastic deformation. This approach addresses the challenges of interpreting internal variables, lack of direct measurement, and weak physical underpinning in classical plasticity models.
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