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Poster
in
Workshop: Machine Learning and the Physical Sciences

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion

Ankita Shukla · Rushil Anirudh · Eugene Kur · Jayaraman Thiagarajan · Timo Bremer · Brian K Spears · Tammy Ma · Pavan Turaga


Abstract:

In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hyperspherical generative model that requires computationally inefficient sampling from distributions like the von Mis Fisher, we sample from a normal distribution followed by a projection layer before the generator. Finally, to determine the validity of the generated samples, we exploit a known relationship between the modalities in the dataset as a scientific constraint, and study different properties of the proposed model.

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