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Ludger Paehler
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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
542021
Sparse identification of truncation errors
S Thaler, L Paehler, NA Adams
Journal of Computational Physics 397, 108851, 2019
352019
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
142022
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
62023
Koopman-assisted reinforcement learning
P Rozwood, E Mehrez, L Paehler, W Sun, SL Brunton
arXiv preprint arXiv:2403.02290, 2024
42024
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
22023
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
22023
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
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