1 citations · 1 across the 5 of their papers we have counts for
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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…
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…
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning
Georg Kruse, Dominik Happel, Stefan Ditze +2
The optimization of electrical circuits is a difficult and time-consuming process performed by experts, but also increasingly by sophisticated algorithms. In this paper, a reinforc…