Introduces a scalable Bayesian inference framework for nonlinear conservation laws using Gaussian process priors and sparse approximations, enabling accurate forward simulations with UQ and fast posterior recovery on inverse problems.
Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes
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Scalable Bayesian Inference for Nonlinear Conservation Laws
Introduces a scalable Bayesian inference framework for nonlinear conservation laws using Gaussian process priors and sparse approximations, enabling accurate forward simulations with UQ and fast posterior recovery on inverse problems.