Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion

• URL: http://arxiv.org/abs/2111.12798v1
• Date: Wed, 24 Nov 2021 21:06:36 GMT
• Title: Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
• Authors: Ankita Shukla, Rushil Anirudh, Eugene Kur, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears, Tammy Ma, Pavan Turaga
• Abstract summary: We develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data. We exploit a known relationship between the modalities in the dataset as a scientific constraint, and study different properties of the proposed model.
• Score: 32.1427322437781
• License: http://creativecommons.org/licenses/by/4.0/
• 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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