| Respecting causality for training physics-informed neural networks S Wang, S Sankaran, P Perdikaris Computer Methods in Applied Mechanics and Engineering 421, 116813, 2024 | 352 | 2024 |
| An expert's guide to training physics-informed neural networks S Wang, S Sankaran, H Wang, P Perdikaris arXiv preprint arXiv:2308.08468, 2023 | 137 | 2023 |
| Hodlrlib: A library for hierarchical matrices S Ambikasaran, KR Singh, SS Sankaran Journal of Open Source Software 4 (34), 1167, 2019 | 18 | 2019 |
| 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 | 11* | 2024 |
| 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 | 11 | |
| 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 |
| An expert’s guide to training physics-informed neural networks (2023) S Wang, S Sankaran, H Wang, P Perdikaris Preprint at https://arxiv. org/pdf/2308.08468. pdf, 0 | 7 | |
| An Expert’s Guide to Training Physics-informed Neural Networks, arXiv S Wang, S Sankaran, H Wang, P Perdikaris preprint, 2023 | 5 | 2023 |
| Respecting Causality Is All You Need for Training Physics-Informed Neural Networks. Mar. 2022. DOI: 10. 48550 S Wang, S Sankaran, P Perdikaris arXiv preprint arXiv.2203.07404, 0 | 5 | |
| Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials S Wang, TR Liu, S Sankaran, P Perdikaris arXiv preprint arXiv:2410.05281, 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 |
