Oct 15, 2023 · We introduce a fully probabilistic approach to simultaneously learn the forward and inverse maps of parametric PDEs.
Aug 9, 2022 · We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) ...
We propose a probabilistic learning objective in which weighted residuals of the governing PDE are used as virtual observables, which are combined with the ...
A new class of spatially stochastic physics and data informed deep latent models for parametric partial differential equations (PDEs) which operate through ...
Our probabilistic model explicitly incorporates a parametric PDE-based density and a trainable solution-to-parameter network while the introduced amortized ...
Fully probabilistic deep models for forward and inverse problems in parametric PDEs · Random grid neural processes for parametric partial differential equations.
Aug 7, 2023 · New paper! “Fully probabilistic deep models for forward and inverse problems in parametric PDEs”. Published in the Journal of Computational ...
Aug 9, 2022 · The proposed framework further allows for a seamless integration of observed data for solving inverse problems and building generative models.
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In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by ...
Fully probabilistic deep models for forward and inverse problems in parametric PDEs. Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark ...