40 citations · 103 across the 5 of their papers we have counts for
5 papers
Deep learning based Meta-modeling for Multi-objective Technology Optimization of Electrical Machines
Vivek Parekh, Dominik Flore, Sebastian Schöps
Optimization of rotating electrical machines is both time- and computationally expensive. Because of the different parametrization, design optimization is commonly executed separat…
Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach
Vivek Parekh, Dominik Flore, Sebastian Schöps +1
Magneto-static finite element (FE) simulations make numerical optimization of electrical machines very time-consuming and computationally intensive during the design stage. In this…
Performance Analysis of Electrical Machines Using a Hybrid Data- and Physics-Driven Model
Vivek Parekh, Dominik Flore, Sebastian Schöps
In the design phase of an electrical machine, finite element (FE) simulation are commonly used to numerically optimize the performance. The output of the magneto-static FE simulati…
Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines
Vivek Parekh, Dominik Flore, Sebastian Schöps
Conventional magneto-static finite element analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of…
Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine
Vivek Parekh, Dominik Flore, Sebastian Schöps
The design of an electrical machine can be quantified and evaluated by Key Performance Indicators (KPIs) such as maximum torque, critical field strength, costs of active parts, sou…