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Learning Functional Priors and Posteriors from Data and Physics
In summary, the proposed method is capable of learning flexible functional priors, and can be extended to big data problems using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
We present real-world use cases that range from challenging forward multi-physics simulations with turbulence and complex 3D geometries, to industrial design optimization and inverse problems that are not addressed efficiently by the traditional solvers.
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