Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
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Abstract (arXiv)
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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References · 9
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
- 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 684
- 2018Spherical CNNscited by 456
- 2017Wasserstein Auto-Encoderscited by 373
- 2019Hyperbolic Image Embeddingscited by 221
- 2018Hyperspherical Variational Auto-Encoderscited by 189
- 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
- 2017CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networkscited by 17
- 2019Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistenciescited by 14
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