Wildland fires are a severe natural hazard, demanding advanced solutions for prevention, monitoring, and response. This work introduces a physics-informed neural network (PiNN) to learn unknown parameters of a wildfire spreading model by integrating fundamental physical laws like mass and energy conservation. Using synthetic firefront data and real-world thermal images (e.g., the 2002 Troy Fire, California), the PiNN successfully predicts key model parameters for 1D and 2D wildfire scenarios. The framework demonstrates robustness, maintaining accuracy even with noisy data. This PiNN-powered approach advances digital twin technology, enabling smarter wildfire management through data-driven, physics-informed predictions.
website: popularscientist.com
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#PhysicsInformedAI
#DigitalTwin
#NeuralNetworks
#WildfirePrevention
#DataDrivenScience
#AIforGood
#EnvironmentalAI
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