| 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 | 155 | 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 | 29 | 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 | 20 | 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 | 10 | 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 |
