Software Open Access
Neural Solvers
Stiller, Patrick;
Zhdanov, Maksim;
Rustamov, Jeyhun;
Bethke, Friedrich;
Hoffmann, Nico
Neural Solvers are neural network-based solvers for partial differential equations and inverse problems. The framework implements scalable physics-informed neural networks Physics-informed neural networks allow strong scaling by design. Therefore, we have developed a framework that uses data parallelism to accelerate the training of physics-informed neural networks significantly. To implement data parallelism, we use the Horovod framework, which provides near-ideal speedup on multi-GPU regimes.
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NeuralSolvers-v.0.1.zip
md5:e5c607ca66ca5486779e58f186e84d5b |
1.6 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 684 | 684 |
| Downloads | 18 | 18 |
| Data volume | 28.1 MB | 28.1 MB |
| Unique views | 530 | 530 |
| Unique downloads | 18 | 18 |
- Publication date:
- September 6, 2021
- DOI:
-
- Keyword(s):
- PINNs PDEs Neural Solver Scalable AI
- Related identifiers:
- Compiled by:
https://arxiv.org/pdf/2009.03730.pdf - Identical to:
https://www.hzdr.de/publications/Publ-33172 - Communities:
- License (for files):
- Creative Commons Attribution 1.0 Generic
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Cite as
Stiller, Patrick, Zhdanov, Maksim, Rustamov, Jeyhun, Bethke, Friedrich, & Hoffmann, Nico. (2021, September 6). Neural Solvers (Version 0.1). Rodare. http://doi.org/10.14278/rodare.1194