| Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 117 | 2023 |
| Group equivariant fourier neural operators for partial differential equations J Helwig, X Zhang, C Fu, J Kurtin, S Wojtowytsch, S Ji arXiv preprint arXiv:2306.05697, 2023 | 32 | 2023 |
| SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations X Zhang, J Helwig, Y Lin, Y Xie, C Fu, S Wojtowytsch, S Ji arXiv preprint arXiv:2403.19507, 2024 | 9 | 2024 |
| Artificial intelligence for science in quantum, atomistic, and continuum systems. arXiv 2023 X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423 10, 2023 | 8 | 2023 |
| Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency Y Lin, J Helwig, S Gui, S Ji arXiv preprint arXiv:2406.07598, 2024 | 4 | 2024 |
| An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling S Dasgupta, P Zhao, J Helwig, P Ghosh, D Pati, BK Mallick arXiv preprint arXiv:2303.08979, 2023 | 3 | 2023 |
| A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils J Helwig, X Zhang, H Yu, S Ji arXiv preprint arXiv:2412.09399, 2024 | | 2024 |
| Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction C Fu, J Helwig, S Ji Learning on Graphs Conference, 36: 1-36: 19, 2024 | | 2024 |
| Algorithm xxx: A Covariate-Dependent Approach to Gaussian Graphical Modeling in R J Helwig, S Dasgupta, P Zhao, BK Mallick, D Pati ACM Transactions on Mathematical Software, 2024 | | 2024 |
| Package ‘covdepGE’ J Helwig, S Dasgupta, P Zhao, B Mallick, D Pati, MJ Helwig | | 2022 |
| Learning Time-Dependent Density Functional Theory via Geometry and Physics Aware Latent Evolution X Zhang, J Helwig, H Yu, X Qian, S Ji | | |
