Author

Julian P. Merkofer

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#machine learning May 2025

Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy

A Bayesian inference framework using Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations, enhancing quantification reliability, and a physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations.

Julian P. Merkofer, D. V. D. van de Sande, A. Bhogal et al. · arXiv.org · 0 citations

#machine learning Preprint Sep 2026

Learning Array Signal Topologies as Conditional Neural Manifolds

The conditional neural manifold is proposed, which replaces the fixed manifold with an observation-conditioned mapping from source parameters to steering vectors, and resolves the angle-frequency ambiguity inherent to the nominal spatial manifold.

Julian P. Merkofer, Vincent van de Schaft, R. V. van Sloun · 0 citations