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.
Předmět:	FOS: Computer and information sciences Computer Science - Machine Learning ComputingMethodologies_PATTERNRECOGNITION Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Machine Learning (cs.LG)
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
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48715514fcb6902efb7874b925604811 https://arxiv.org/abs/2111.12798 Zobrazit plný text záznamu