# Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks

arXiv: https://arxiv.org/abs/2502.12177 · HTML: https://mlsanity.com/paper/2502.12177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [Physics-Informed Neural Embeddings of PDE Solution Families](https://mlsanity.com/paper/2607.06348/) (2026)
- [Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices](https://mlsanity.com/paper/2609.11628/) (2026)

## Abstract (arXiv)

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.