mlsanityData to 7 Oct 2026

Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression

arXiv 2211.10360 ↗First posted 16 Nov 2022cs.LG cs.AI cs.NAcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 8 of its 51 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

8 of 51 references.

References · 8

  1. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
  2. 2020A Survey of Deep Active Learningcited by 547
  3. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  4. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  5. 2019Diverse mini-batch Active Learningcited by 88
  6. 2018Dropout-based Active Learning for Regressioncited by 24
  7. 2022Rare event failure test case generation in Learning-Enabled-Controllerscited by 13
  8. 2019Learning to design from humans: Imitating human designers through deep learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 2; 2025: 1; 2026: 2.

11 papers.

  1. 2023Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Designcited by 9
  2. 2023Search for universal minimum drag resistance underwater vehicle hull using CFDcited by 8
  3. 2023Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hullscited by 4
  4. 2023Vehicle lateral control using Machine Learning for automated vehicle guidancecited by 2
  5. 2025Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modellingcited by 2
  6. 2024Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledgecited by 1
  7. 2026Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Trainingcited by 1
  8. 2023Systematic design space exploration by learning the explored space using Machine Learningcited by 0
  9. 2023Optimization for truss design using Bayesian optimizationcited by 0
  10. 2024Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD toolcited by 0
  11. 2026PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulationcited by 0