2 papers
physics.comp-ph2026
Resolving positive semi-definiteness in physics-informed kernels for scientific machine learning
J. Moser, C. Albert, S. Ranftl
Many modern machine learning models can be understood as kernel-based function-space models, including Gaussian processes and neural tangent kernels. In scientific machine learning…
cs.LG2026
Deep Polynomial Chaos Expansion
Johannes Exenberger, Sascha Ranftl, Robert Peharz
Polynomial chaos expansion (PCE) is a classical and widely used surrogate modeling technique in physical simulation and uncertainty quantification. By taking a linear combination o…