Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Physics-informed fine-tuning of foundation models for partial differential equations
Vlad Medvedev, Leon Armbruster, Christopher Straub +2
Foundation models for partial differential equations (PDEs) have emerged as powerful surrogates pre-trained on diverse physical systems, but adapting them to new downstream tasks r…
cs.LG2025
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
Christopher Straub, Philipp Brendel, Vlad Medvedev +1
We present a novel approach to hard-constrain Neumann boundary conditions in physics-informed neural networks (PINNs) using Fourier feature embeddings. Neumann boundary conditions…