Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12177 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912235630100480 |
|---|---|
| author | Liu, Shuheng Protopapas, Pavlos Sondak, David Chen, Feiyu |
| author_facet | Liu, Shuheng Protopapas, Pavlos Sondak, David Chen, Feiyu |
| contents | Solving differential equations is a critical challenge across a host of domains. While many software packages efficiently solve these equations using classical numerical approaches, there has been less effort in developing a library for researchers interested in solving such systems using neural networks. With PyTorch as its backend, NeuroDiffEq is a software library that exploits neural networks to solve differential equations. In this paper, we highlight the latest features of the NeuroDiffEq library since its debut. We show that NeuroDiffEq can solve complex boundary value problems in arbitrary dimensions, tackle boundary conditions at infinity, and maintain flexibility for dynamic injection at runtime. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks Liu, Shuheng Protopapas, Pavlos Sondak, David Chen, Feiyu Machine Learning Solving differential equations is a critical challenge across a host of domains. While many software packages efficiently solve these equations using classical numerical approaches, there has been less effort in developing a library for researchers interested in solving such systems using neural networks. With PyTorch as its backend, NeuroDiffEq is a software library that exploits neural networks to solve differential equations. In this paper, we highlight the latest features of the NeuroDiffEq library since its debut. We show that NeuroDiffEq can solve complex boundary value problems in arbitrary dimensions, tackle boundary conditions at infinity, and maintain flexibility for dynamic injection at runtime. |
| title | Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.12177 |