AI RESEARCH
Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
arXiv CS.LG
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ArXi:2605.04708v1 Announce Type: new From neural ODEs to continuous-time machine learning, differentiable solvers allow physics, optimization, and simulation to become trainable components within deep learning systems. This has opened the path to a new generation of deep learning frameworks for scientific computing, with many promising applications still emerging. In this paper, we integrate a differentiable chemistry solver into a modified physics-informed neural network to solve parameterized reaction systems that are inherently stiff. The proposed framework.