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First published on Tuesday, Jun 2, 2026 and last modified on Thursday, Jul 30, 2026 by François Chaplais.
WebMagic
The purpose of this demonstration is to build some documentation about Physics-Informed Neural Networks, or PINNs. These are presented in the introduction below.
You can also consult the Wikipedia page on the subject.
1 Introduction
The content of this section is directly imported from the following two documents:
Any change in the original documents will be reflected in this document after HTML export.
\refInput{An-overview-of-PINNs-with-AI/PINN-presentation} \refInput{A-sample-PINN-code-from-AI/PINN-sample-code}
2 Proposed bibliography
We have stored here the links to a few arXiv papers.
- PINNs Failure Modes are Overfitting
- Cosmo-PINN: A Physics-Informed Neural Network for Cosmological Reconstruction
- Alt-CC-PINN: An Alternating Optimization Framework with Implicit Neural Representation for Microwave Inverse Scattering Imaging
- Mitigating Gradient Pathology in PINNs through Aligned Constraint
- Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
- When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
- Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
- Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
You can get the source of the articles by visiting the links and select "TeX source" in the upper right corner of the page.
We have already imported some.
3 The plan
3.1 Import a new document from the list
We are going to proceed as follows.
- Import a document.
- Read it and grab some interesting section references.
- In this very document, create a new section devoted to the new document.
- In this new section explain the purpose of the article and insert the section references that we have collected.
- Go back to the new article, prepare it for AI and ask the AI for a summary, and copy the LaTeX code for the answer into the document.
The document will be Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems .
Here is the BibTeX entry.
@misc{babić2026differentiablechemistrypinnssolving,
title={Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems Demo},
author={Miloš Babić and Franz M. Rohrhofer and Stefan Posch},
year={2026},
eprint={2605.04708},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2605.04708},
}3.2 Display some data about PINNs
The data comes from Kaggle . Here is what the author says about the data.
The dataset was designed to support the development, training, and validation of physics-informed graph neural network (PI-GNN) models for electrochemical CO2 reduction (eCO2R) reactors. Its primary purpose is to bridge the gap between high-fidelity multiphysics simulations, limited experimental measurements, and data-efficient machine learning, enabling accurate prediction, scale-up analysis, and optimization of electrochemical reactors under realistic operating conditions. The dataset structure explicitly reflects the graph-based representation of reactor domains, where each graph corresponds to a discretized reactor instance with variable resolution (500–5,000 nodes), accommodating differences in reactor geometry, mesh density, and operating regime.
We will upload the CSV data and insert a dynamic table built around this CSV data.