| Respecting causality is all you need for training physics-informed neural networks S Wang, S Sankaran, P Perdikaris arXiv preprint arXiv:2203.07404, 2022 | 203 | 2022 |
| An expert's guide to training physics-informed neural networks S Wang, S Sankaran, H Wang, P Perdikaris arXiv preprint arXiv:2308.08468, 2023 | 52 | 2023 |
| Respecting causality for training physics-informed neural networks S Wang, S Sankaran, P Perdikaris Computer Methods in Applied Mechanics and Engineering 421, 116813, 2024 | 27 | 2024 |
| Hodlrlib: A library for hierarchical matrices S Ambikasaran, KR Singh, SS Sankaran Journal of Open Source Software 4 (34), 1167, 2019 | 17 | 2019 |
| Respecting causality is all you need for training physics-informed neural networks, arXiv S Wang, S Sankaran, P Perdikaris arXiv preprint arXiv:2203.07404, 2022 | 8 | 2022 |
| On the impact of larger batch size in the training of physics informed neural networks S Sankaran, H Wang, LF Guilhoto, P Perdikaris The Symbiosis of Deep Learning and Differential Equations II, 2022 | 7 | 2022 |
| Respecting causality is all you need for training physics-informed neural networks, 2022 S Wang, S Sankaran, P Perdikaris URL https://arxiv. org/abs/2203.07404, 0 | 6 | |
| Bridging Operator Learning and Conditioned Neural Fields: A Unifying Perspective S Wang, JH Seidman, S Sankaran, H Wang, GJ Pappas, P Perdikaris arXiv preprint arXiv:2405.13998, 2024 | 1 | 2024 |
| Viscoelastic Free Surface Flows: From Models to Experiments and Somewhere in Between. R Rao, D Bolintineanu, W Ortiz, S Sankaran, P Perdikaris, W Hartt, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
