# DADO -- Low-Cost Query Strategies for Deep Active Design Optimization

arXiv: https://arxiv.org/abs/2307.04536 · HTML: https://mlsanity.com/paper/2307.04536/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing](https://mlsanity.com/paper/1903.10145/) (2019)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain](https://mlsanity.com/paper/2003.04685/) (2020)
- [Active Learning with Partial Feedback](https://mlsanity.com/paper/1802.07427/) (2018)
- [Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks](https://mlsanity.com/paper/2206.14092/) (2022)
- [Self-Directed Online Machine Learning for Topology Optimization](https://mlsanity.com/paper/2002.01927/) (2020)
- [Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines](https://mlsanity.com/paper/2201.08877/) (2022)
- [Dataset of a parameterized U-bend flow for Deep Learning Applications](https://mlsanity.com/paper/2305.05216/) (2023)

## Built on by (most cited first, 4 of 4)

- [Towards Deep Active Learning in Avian Bioacoustics](https://mlsanity.com/paper/2406.18621/) (2024)
- [Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation](https://mlsanity.com/paper/2309.13179/) (2023)
- [An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing](https://mlsanity.com/paper/2406.00080/) (2024)
- [From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs](https://mlsanity.com/paper/2406.00081/) (2024)

## Abstract (arXiv)

In this experience report, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations. We are interested in optimizing the design of structural components, where the shape is described by a set of parameters. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two selection strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our selection strategies in accelerating design optimization. We believe that the introduced method is easily transferable to other self-optimization problems.