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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.
Author Information
Ankita Shukla (Arizona State University)
Rushil Anirudh (Lawrence Livermore National Laboratory)
Eugene Kur (Lawrence Livermore National Laboratory)
Jayaraman Thiagarajan (Lawrence Livermore National Labs)
Timo Bremer (Lawrence Livermore National Laboratory)
Brian K Spears (Lawrence Livermore National Laboratory)
Brian Spears is a target design physicist in the inertial confinement fusion (ICF) program at Lawrence Livermore National Laboratory. His current work focuses on the intersection of experimental science, high-performance simulation, and machine learning. Brian leads a strategic initiative aimed at developing deep learning methods for Laboratory missions. He has designed ICF experiments for almost 15 years, including the first cryogenic layered experiments at the National Ignition Facility. He developed new ICF ignition metrics using the first large-scale ensembles of 2D ICF simulations. Brian completed his PhD at the University of California, Berkeley where he studied topological methods for high-dimensional dynamical systems. He also holds a BS in mechanical engineering and a BA in liberal arts from the University of Texas at Austin. When not doing science, he can be found racing his bike or chauffeuring his two daughters to swim and gymnastics.
Tammy Ma (Lawrence Livermore National Laboraotry)
Pavan Turaga (Arizona State University)
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