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Simulations of Large-scale Wildfire Scenarios using Tensorflow Compute Architectures

John Roberts Anderson
Matthias Ihme
Yi-fan Chen
Google Scholar


This work presents a high-fidelity simulation framework for modeling large-scale wildfire scenarios that take into consideration realistic topographies, atmospheric conditions, turbulence/fire interaction, and flow dynamics. With the overall goal of enabling large-scale ensemble simulations and the integration of the simulation results into machine-learning applications, this modeling framework has been implemented into the TensorFlow programming environment. To demonstrate the capability of this simulation framework in predicting large-scale fires, we performed high-resolution simulations of a realistic wildfire scenario that is representative of the 2017 Tubbs fire.

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