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Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems

arXiv 2605.04708 ↗First posted 6 May 2026cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 19

  1. 2018Neural Ordinary Differential Equationscited by 3,733
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  6. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  7. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 516
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  10. 2022Respecting causality is all you need for training physics-informed neural networkscited by 154
  11. 2021Encoding physics to learn reaction-diffusion processescited by 61
  12. 2021Learning in Sinusoidal Spaces with Physics-Informed Neural Networkscited by 61
7 more
  1. 2021Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networkscited by 43
  2. 2024Parameterized Physics-informed Neural Networks for Parameterized PDEscited by 35
  3. 2022Learning Stochastic Dynamics with Statistics-Informed Neural Networkcited by 16
  4. 2024A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformationscited by 10
  5. 2022Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Datacited by 5
  6. 2024Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networkscited by 5
  7. 2025PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamicscited by 5

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