Shape optimization in laminar flow with a label-guided variational autoencoder

2017/12/10 by Stephan Eismann, Eismann, Stephan, Stefan Bartzsch +3 · 1 citation
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Building Energy and Comfort Optimization #Computational Engineering #FOS: Computer and information sciences #Finance #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.1712.03599

openalex publication_date 2017/12/10 · openalex created_date 2017/12/22 · openalex updated_date 2026/07/28

Abstract

Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically. In this work, we explore a Bayesian optimization approach to minimize an object's drag coefficient in laminar flow based on predicting drag directly from the object shape. Jointly training an architecture combining a variational autoencoder mapping shapes to latent representations and Gaussian process regression allows us to generate improved shapes in the two dimensional case we consider.

Cited by

• Active Learning over DNN: Automated Engineering Design Optimization for Fluid Dynamics Based on Self-Simulated Dataset

Related

• Using Large Language Models for Parametric Shape Optimization
• A supervised neural network for drag prediction of arbitrary 2D shapes in low Reynolds number flows
• Direct shape optimization through deep reinforcement learning
• TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning