  Wildland fires pose terrifying natural hazards, 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) to
learn the unknown parameters of an interpretable wildfire spreading model. The
considered wildfire spreading model integrates fundamental physical laws
articulated by key model parameters, essential for capturing the complex
behavior of wildfires. The proposed machine learning approach leverages the
theory of artificial neural networks with the physical constraints governing
wildfire dynamics, such as the first principles of mass and energy
conservation. Training of the PiNN for physics-informed parameter
identification is realized using data of the temporal evolution of one- and
two-dimensional (plane surface) fire fronts that have been obtained from a
high-fidelity simulator of the wildfire spreading model under consideration.
The parameter learning results demonstrate the remarkable predictive ability of
the proposed PiNN in uncovering the unknown coefficients in both the one- and
two-dimensional fire spreading scenarios. Additionally, this methodology
exhibits robustness by identifying the same parameters in the presence of noisy
data. The proposed framework is envisioned to be incorporated in a
physics-informed digital twin for intelligent wildfire management and risk
assessment.
