Summary of Physics-informed Neural Networks For Parameter Learning Of Wildfire Spreading, by Konstantinos Vogiatzoglou et al.
Physics-informed neural networks for parameter learning of wildfire spreading
by Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou, Konstantinos Ampountolas
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a novel machine learning model called a Physics-Informed Neural Network (PiNN) to learn unknown parameters of an interpretable wildfire spreading model. The PiNN combines the theory of artificial neural networks with fundamental physical laws governing wildfire dynamics, including mass and energy conservation. The model is trained using synthetic data from a high-fidelity simulator and empirical data from the Troy Fire in California. Results show that the PiNN can accurately identify unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios. Additionally, the model demonstrates robustness by identifying the same parameters even with noisy data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop a digital twin for wildfire prevention, monitoring, intervention, and response using machine learning. A Physics-Informed Neural Network (PiNN) is designed to learn unknown parameters of an interpretable wildfire spreading model. The PiNN combines physical laws governing wildfire dynamics with artificial neural networks. The model is trained using synthetic data from a high-fidelity simulator and empirical data from the Troy Fire in California. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Synthetic data
