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Poster
in
Workshop: NeurIPS 2024 Workshop: Machine Learning and the Physical Sciences

Shaping Flames with Differentiable Physics Simulations

Laura Leja · Kārlis Freivalds · Oskars Teikmanis


Abstract:

We introduce a method for shaping simulated flames into customisable forms, with the goal of overcoming the limitations of current pyrotechnic systems, which are typically restricted to producing basic fire columns with minimal control over the resulting shape. Our approach leverages differentiable physics and combustion simulations. We demonstrate the use of differentiable physics-based training to produce simulated letter-shaped flames, and take initial steps towards implementing this method on real flame projectors by aligning the simulation with a physical device. The ability to control flame shape would significantly expand the possibilities for stage pyrotechnics and creative applications in performance art.

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