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Found 3 papers, 1 papers with code
Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation
This paper presents a methodological framework for training, self-optimising, and self-organising surrogate models to approximate and speed up multiobjective optimisation of technical systems based on multiphysics simulations.
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization
We present two selection strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems.
Dataset of a parameterized U-bend flow for Deep Learning Applications
This third representation enables considering the specific data structure of numerical simulations for deep learning approaches.
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