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
Publications
1. N. Vlassis, Ran Ma, W.C. Sun, Geometric deep learning for computational mechanics Part I: Anisotropic Hyperelasticity, Computer Methods in Applied Mechanics and Engineering 371: 113299, doi:10.1016/j.cma.2020.113299, 2020.
2. N. Vlassis, W.C. Sun, Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening, Computer Methods in Applied Mechanics and Engineering 377 : 113695, doi:10.1016/j.cma.2021.113695, 2021.
3. N. Vlassis, W.C. Sun, Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models, Journal of Applied Mechanics 89.2:021003, https://doi.org/10.1115/1.4052684, 2021.
4. X. Sun, B. Bahmani, N. Vlassis, W.C. Sun, Y. Xu, Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty quantification, Granular Matter 24: 1, https://doi.org/10.1007/s10035-021-01137-y, 2021.
5. C. Cai, N. Vlassis, B. Bahmani, L. Magee, R. Ma, Z. Xiong, Y. Wang, W.C. Sun, Equivariant Geometric learning for digital rock physics: formation factor and effective permeability, International Journal for Multiscale Computation and Engineering, 2021.
6. N. Vlassis, P. Zhao, R. Ma, T. Sewell, W.C. Sun, Molecular dynamics inferred transfer learning models for finite-strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints, International Journal for Numerical Methods in Engineering, Volume 123, Issue 17, Featured Cover, https://doi.org/10.1002/nme.6992, 2022.
7. P. Nguyen, N. Vlassis, B. Bahmani, W.C. Sun, H.S. Udaykumar, S. Baek, Synthesizing Controlled Microstructures of Porous Media using Generative Adversarial Networks and Reinforcement Learning, Scientific Reports, https://doi.org/10.1038/s41598-022-12845-7, 2022.
8. N. Vlassis, W.C. Sun, Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity, Computer Methods in Applied Mechanics and Engineering, 404: 115768, https://doi.org/10.1016/j.cma.2022.115768, 2023.
9. R. Villarreal, N. Vlassis, N. Phan, T. Catanach, R.E. Jones, N. Trask, S.L. Karmer, W.C. Sun, Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter, Computational Mechanics, https://doi.org/10.1007/s00466-023-02335-6, 2023.
10. N. Vlassis, W.C. Sun, Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties, Computer Methods in Applied Mechanics and Engineering, 413:116126, https://doi.org/10.1016/j.cma.2023.116126, 2023.
11. N. Vlassis, W.C. Sun, K.A. Alshibli, R.A. Regueiro, Synthesizing realistic sand assemblies with denoising diffusion in latent space, under review, preprint, 2023.