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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…
cs.LG2023
Continuously Adapting Random Sampling (CARS) for Power Electronics Parameter Design
Dominik Happel, Philipp Brendel, Andreas Rosskopf +1
To date, power electronics parameter design tasks are usually tackled using detailed optimization approaches with detailed simulations or using brute force grid search grid search…