NeuroDiffEq用神经网络求解复杂微分方程，支持任意维和无穷边界。
Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks
- 基于PyTorch，用神经网络逼近微分方程解
- 可处理任意维度边界值问题及无穷远边界条件
- 支持运行时动态注入新条件，适合研究者快速实验
求解微分方程是多个领域中的关键挑战。尽管已有众多软件包通过经典数值方法高效求解，但针对使用神经网络求解此类系统的研究工具仍较匮乏。NeuroDiffEq是以PyTorch为后端的开源库，利用神经网络求解微分方程。本文介绍了NeuroDiffEq自发布以来的最新特性：该库可求解任意维度的复杂边界值问题，处理无穷远处的边界条件，并支持运行时动态注入新条件，保持高度灵活性。
原文摘要 · Abstract (English)
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.
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