Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
Abstract
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 introduces several key components required to overcome limitations of standard physics-informed neural networks. These include a differentiable chemistry solver, a network architecture for parameterized solutions, and residual weighting tailored to stiff reactions. We evaluate the framework on a set of differential equations related to hydrogen combustion, which include initial/boundary value problems, inverse parameter identification, and a parameterized partial differential equation. Our results highlight the ability of the proposed approach to extend physics-informed neural networks to stiff chemical systems that were previously inaccessible.
Cite
@article{arxiv.2605.04708,
title = {Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems},
author = {Miloš Babić and Franz M. Rohrhofer and Stefan Posch},
journal= {arXiv preprint arXiv:2605.04708},
year = {2026}
}