Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
This addresses the challenge of expensive simulations in engineering design optimization, though it is incremental as it builds on existing active learning methods.
The paper tackles the problem of reducing the computational cost of building accurate surrogate models for engineering design by introducing epsilon HQS, a scalable active learning strategy that uses a student-teacher framework to train deep neural networks efficiently, achieving higher accuracy under fixed labeling cost budgets in CFD, FEA, and propeller design tasks.
High fidelity design evaluation processes such as Computational Fluid Dynamics and Finite Element Analysis are often replaced with data driven surrogates to reduce computational cost in engineering design optimization. However, building accurate surrogate models still requires a large number of expensive simulations. To address this challenge, we introduce epsilon HQS, a scalable active learning strategy that leverages a student teacher framework to train deep neural networks efficiently. Unlike Bayesian AL methods, which are computationally demanding with DNNs, epsilon HQS selectively queries informative samples to reduce labeling cost. Applied to CFD, FEA, and propeller design tasks, our method achieves higher accuracy under fixed labeling cost budgets.