Continuously Adapting Random Sampling (CARS) for Power Electronics
  Parameter Design
Dominik Happel Philipp Brendel Andreas Rosskopf Stefan Ditze
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 with very fast simulations. A new method, named
"Continuously Adapting Random Sampling" (CARS) is proposed, which provides a
continuous method in between. This allows for very fast, and / or large amounts
of simulations, but increasingly focuses on the most promising parameter
ranges. Inspirations are drawn from multi-armed bandit research and lead to
prioritized sampling of sub-domains in one high-dimensional parameter tensor.
Performance has been evaluated on three exemplary power electronic use-cases,
where resulting designs appear competitive to genetic algorithms, but
additionally allow for highly parallelizable simulation, as well as continuous
progression between explorative and exploitative settings.