# Physics-informed neural networks for parameter learning of wildfire spreading

arXiv: https://arxiv.org/abs/2406.14591 · HTML: https://mlsanity.com/paper/2406.14591/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 78 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [DeepXDE: A deep learning library for solving differential equations](https://mlsanity.com/paper/1907.04502/) (2019)
- [Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems](https://mlsanity.com/paper/2111.02801/) (2021)
- [Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires](https://mlsanity.com/paper/2212.00970/) (2022)
- [Parameter Estimation of Fire Propagation Models Using Level Set Methods](https://mlsanity.com/paper/2006.16550/) (2020)
- [On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniques](https://mlsanity.com/paper/2303.00187/) (2023)

## Built on by (most cited first, 6 of 6)

- [Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network](https://mlsanity.com/paper/2507.08749/) (2025)
- [Physics-Informed Regression: Parameter Estimation in Parameter-Linear Nonlinear Dynamic Models](https://mlsanity.com/paper/2508.19249/) (2025)
- [Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities](https://mlsanity.com/paper/2410.04096/) (2024)
- [Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction](https://mlsanity.com/paper/2609.17763/) (2026)
- [Physics-Constrained Denoising Autoencoders for Data-Scarce Wildfire UAV Sensing](https://mlsanity.com/paper/2601.11794/) (2026)
- [Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations](https://mlsanity.com/paper/2609.18555/) (2026)

## Abstract (arXiv)

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies.