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

arXiv: https://arxiv.org/abs/2211.10360 · HTML: https://mlsanity.com/paper/2211.10360/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Diverse mini-batch Active Learning](https://mlsanity.com/paper/1901.05954/) (2019)
- [Dropout-based Active Learning for Regression](https://mlsanity.com/paper/1806.09856/) (2018)
- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [Learning to design from humans: Imitating human designers through deep learning](https://mlsanity.com/paper/1907.11813/) (2019)

## Built on by (most cited first, 11 of 11)

- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Search for universal minimum drag resistance underwater vehicle hull using CFD](https://mlsanity.com/paper/2302.09441/) (2023)
- [Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls](https://mlsanity.com/paper/2304.12420/) (2023)
- [Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling](https://mlsanity.com/paper/2508.00843/) (2025)
- [Vehicle lateral control using Machine Learning for automated vehicle guidance](https://mlsanity.com/paper/2303.08187/) (2023)
- [Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training](https://mlsanity.com/paper/2606.09949/) (2026)
- [Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge](https://mlsanity.com/paper/2404.16893/) (2024)
- [Systematic design space exploration by learning the explored space using Machine Learning](https://mlsanity.com/paper/2303.08249/) (2023)
- [Optimization for truss design using Bayesian optimization](https://mlsanity.com/paper/2306.01763/) (2023)
- [Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool](https://mlsanity.com/paper/2407.02519/) (2024)
- [PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation](https://mlsanity.com/paper/2608.24056/) (2026)

## 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.