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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.04708 [cs.LG] |
| (or arXiv:2605.04708v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.04708 arXiv-issued DOI via DataCite (pending registration) |
From: Miloš Babić [view email]
[v1]
Wed, 6 May 2026 09:59:05 UTC (388 KB)
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