Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion

Autor:	Shukla, Ankita, Anirudh, Rushil, Kur, Eugene, Thiagarajan, Jayaraman J., Bremer, Peer-Timo, Spears, Brian K., Ma, Tammy, Turaga, Pavan
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
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:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.12798 Zobrazit plný text záznamu View this record from Arxiv