Wildfire simulations have become increasingly important as their frequency and severity increases, posing a threat to communities and resulting in billions in damages. However, current wildfire models face a trade-off between accuracy and computational complexity. Current wildfire models can be physics-based, but computationally expensive, or based on empirical data, which allows for better computational speed, but decreases the physical basis. A model combining the computational speed of empirical models with the physics understanding of physics-based models to increase the accuracy of wildfire simulations is desired. This master thesis explores the design of such a model by answering the question: ’How can a near real-time wildfire simulation be designed using physics-informed machine learning?’....