Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
Autor: | Ankita Shukla, Rushil Anirudh, Eugene Kur, Thiagarajan, Jayaraman J., Peer-Timo Bremer, Spears, Brian K., Tammy Ma, Turaga, Pavan K. |
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Zdroj: | Ankita Shukla |
Popis: | 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. Comment: 5 pages, 4 figures, Fourth Workshop on Machine Learning and the Physical Sciences, NeurIPS 2021 |
Databáze: | OpenAIRE |
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