Reverse-mode automatic differentiation and optimization of GPU kernels via Enzyme WS Moses, V Churavy, L Paehler, J Hückelheim, SHK Narayanan, ... Proceedings of the international conference for high performance computing …, 2021 | 54 | 2021 |
Sparse identification of truncation errors S Thaler, L Paehler, NA Adams Journal of Computational Physics 397, 108851, 2019 | 35 | 2019 |
Scalable automatic differentiation of multiple parallel paradigms through compiler augmentation WS Moses, SHK Narayanan, L Paehler, V Churavy, M Schanen, ... SC22: international conference for high performance computing, networking …, 2022 | 14 | 2022 |
Compile: A large ir dataset from production sources A Grossman, L Paehler, K Parasyris, T Ben-Nun, J Hegna, W Moses, ... arXiv preprint arXiv:2309.15432, 2023 | 6 | 2023 |
Koopman-assisted reinforcement learning P Rozwood, E Mehrez, L Paehler, W Sun, SL Brunton arXiv preprint arXiv:2403.02290, 2024 | 4 | 2024 |
Transparent Checkpointing for Automatic Differentiation of Program Loops Through Expression Transformations M Schanen, SHK Narayanan, S Williamson, V Churavy, WS Moses, ... International Conference on Computational Science, 483-497, 2023 | 2 | 2023 |
On the relationships between graph neural networks for the simulation of physical systems and classical numerical methods AP Toshev, L Paehler, A Panizza, NA Adams arXiv preprint arXiv:2304.00146, 2023 | 2 | 2023 |
HydroGym: A Reinforcement Learning Control Framework for Fluid Dynamics L Paehler, J Callaham, S Ahnert, N Adams, S Brunton Bulletin of the American Physical Society, 2023 | | 2023 |
SIAM CSE 2023 Tutorial: Integrating Scientific Simulations with Machine Learning Algorithms SHK Narayanan, L Paehler, J Hückelheim https://zenodo.org/record/8316283, 2023 | | 2023 |
Improving linear embedding of complex nonlinear flow dynamics N Adams, L Paehler APS Division of Fluid Dynamics Meeting Abstracts, G17. 008, 2019 | | 2019 |
Differentiable Computational Fluid Dynamics L Paehler, J Hueckelheim | | |