Geometric deep learning for computational mechanics part i: Anisotropic hyperelasticity NN Vlassis, R Ma, WC Sun
Computer Methods in Applied Mechanics and Engineering 371, 113299, 2020
159 2020 Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening NN Vlassis, WC Sun
Computer Methods in Applied Mechanics and Engineering 377, 113695, 2021
134 2021 Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning PCH Nguyen, NN Vlassis, B Bahmani, WC Sun, HS Udaykumar, SS Baek
Scientific reports 12 (1), 9034, 2022
30 2022 Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models NN Vlassis, WC Sun
Journal of Applied Mechanics 89 (2), 021003, 2022
24 2022 Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties NN Vlassis, WC Sun
Computer Methods in Applied Mechanics and Engineering 413, 116126, 2023
21 2023 Geometric learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity NN Vlassis, WC Sun
Computer Methods in Applied Mechanics and Engineering 404, 115768, 2023
21 2023 Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation X Sun, B Bahmani, NN Vlassis, WC Sun, Y Xu
Granular Matter 24, 1-32, 2022
18 2022 Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints NN Vlassis, P Zhao, R Ma, T Sewell, WC Sun
International Journal for Numerical Methods in Engineering 123 (17), 3922-3949, 2022
14 2022 Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from morse graph C Cai, N Vlassis, L Magee, R Ma, Z Xiong, B Bahmani, TF Wong, Y Wang, ...
International Journal for Multiscale Computational Engineering 21 (5), 2023
11 2023 Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter R Villarreal, NN Vlassis, NN Phan, TA Catanach, RE Jones, NA Trask, ...
Computational Mechanics 72 (1), 95-124, 2023
6 2023 A neural kernel method for capturing multiscale high-dimensional micromorphic plasticity of materials with internal structures Z Xiong, M Xiao, N Vlassis, WC Sun
Computer Methods in Applied Mechanics and Engineering 416, 116317, 2023
3 2023 Synthesizing realistic sand assemblies with denoising diffusion in latent space NN Vlassis, WC Sun, KA Alshibli, RA Regueiro
arXiv preprint arXiv:2306.04411, 2023
2 2023 MD-inferred neural network monoclinic finite-strain hyperelasticity models for -HMX: Sobolev training and validation against physical constraints NN Vlassis, P Zhao, R Ma, T Sewell, WC Sun
arXiv preprint arXiv:2112.02077, 2021
1 2021 Featured Cover G Massonis, JR Banga, AF Villaverde
International Journal of Robust and Nonlinear Control 9 (33), i-i, 2023
2023 Reinforcement Learning for Material Calibration Via Kalman Filter Estimation. R Villarreal Jr, N VLASSIS, T Catanach, R Jones, N Trask, SLB Kramer, ...
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); Sandia …, 2022
2022 Towards Trustworthy Geometric Deep Learning for Elastoplasticity NN Vlassis
Columbia University, 2021
2021