# AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

arXiv: https://arxiv.org/abs/2502.15643 · HTML: https://mlsanity.com/paper/2502.15643/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 21 Feb 2025.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis](https://mlsanity.com/paper/2306.04066/) (2023)
- [Tackling Multimodal Device Distributions in Inverse Photonic Design using Invertible Neural Networks](https://mlsanity.com/paper/2208.14212/) (2022)
- [Adaptive Partitioning Design and Analysis for Emulation of a Complex Computer Code](https://mlsanity.com/paper/1907.01181/) (2019)

## Abstract (arXiv)

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.