Author

J. Glaubitz

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Preprint Oct 2026

Edge-preserving Bayesian neural networks via hierarchical sparsity-promoting priors

Recovering piecewise-smooth functions with jump discontinuities from noisy, indirect, and incomplete data is a central task in computational inverse problems. Bayesian neural networks (BNNs) with heavy-tailed weight priors have emerged as effective edge-preserving priors for such problems. However, posterior inference...

Jan Habscheid, J. Glaubitz · 0 citations

Preprint Aug 2026

Regularity-informed data assimilation: A hierarchical Bayesian approach to ensemble Kalman filtering for hyperbolic conservation laws

We propose a novel regularity-informed filtering framework for data assimilation in the context of hyperbolic conservation laws and other time-dependent partial differential equations. We focus on systems whose states exhibit steep gradients and jump discontinuities. While filtering is widely used to improve numerical...

J. Glaubitz, Daniel Sharp, Mathieu Le Provost et al. · 2 citations