CONTROLLING SHAPE OF FLAME WITH DIFFERENTIABLE PHYSICS

The study focuses on creating symbol-shaped flame designs by optimizing flame dynamics, using machine learning techniques integrated with advanced combustion modelling. This research addresses the limitations of traditional pyrotechnic systems, which lack precise control over flame shapes, by introducing a method that uses differentiable physics simulations for shaping flames. The method uses the optimization framework PhiFlow and includes radiative heat loss and chemical kinetics for improved accuracy. Machine learning is used not only to train and adapt the model but also to determine the physical parameters that correspond to the actual observed behaviour of the flames. However, it is also important to mathematically and analytically evaluate the results obtained to ensure their accuracy and relevance. Results demonstrate the successful formation of custom flame shapes in a 2D simulated environment, with promising alignment to real-world flame stage projector behaviour. This approach enables control over flame shapes and dynamics, expanding creative possibilities in stage design and interactive art while proving valuable for applications requiring detailed control of dynamic systems, such as pyrotechnics, performance art, and educational demonstrations.