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.
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2026 2verdicts
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A prox-based semi-smooth Newton method is proposed for finite-element discretizations of convex variational problems, with global well-posedness and local superlinear convergence established under suitable assumptions on energy densities.
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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.
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A $\operatorname{prox}$-Based Semi-Smooth Newton Method for Convex Variational Problems
A prox-based semi-smooth Newton method is proposed for finite-element discretizations of convex variational problems, with global well-posedness and local superlinear convergence established under suitable assumptions on energy densities.