# Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design

arXiv: https://arxiv.org/abs/2205.03005 · HTML: https://mlsanity.com/paper/2205.03005/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 6 May 2022.

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

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)
- [PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design](https://mlsanity.com/paper/2106.03620/) (2021)
- [BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks](https://mlsanity.com/paper/2103.05844/) (2021)
- [Integrating Deep Learning into CAD/CAE System: Generative Design and Evaluation of 3D Conceptual Wheel](https://mlsanity.com/paper/2006.02138/) (2020)
- [MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization](https://mlsanity.com/paper/2009.07110/) (2020)

## Built on by (most cited first, 6 of 6)

- [Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design](https://mlsanity.com/paper/2302.02913/) (2023)
- [Constraining Generative Models for Engineering Design with Negative Data](https://mlsanity.com/paper/2306.15166/) (2023)
- [MCD: A Model-Agnostic Counterfactual Search Method For Multi-modal Design Modifications](https://mlsanity.com/paper/2305.11308/) (2023)
- [BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints](https://mlsanity.com/paper/2508.00830/) (2025)
- [Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design](https://mlsanity.com/paper/2206.07170/) (2022)
- [DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation](https://mlsanity.com/paper/2504.11347/) (2025)

## Abstract (arXiv)

Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse design. We propose the Design Target Achievement Index (DTAI), a differentiable, tunable metric that scores a design's ability to achieve designer-specified minimum performance targets. We demonstrate that DTAI can drastically improve the performance of generated designs when directly used as a training loss in Deep Generative Models. We apply the DTAI loss to a Performance-Augmented Diverse GAN (PaDGAN) and demonstrate superior generative performance compared to a set of baseline Deep Generative Models including a Multi-Objective PaDGAN and specialized tabular generation algorithms like the Conditional Tabular GAN (CTGAN). We further enhance PaDGAN with an auxiliary feasibility classifier to encourage feasible designs. To evaluate methods, we propose a comprehensive set of evaluation metrics for generative methods that focus on feasibility, diversity, and satisfaction of design performance targets. Methods are tested on a challenging benchmarking problem: the FRAMED bicycle frame design dataset featuring mixed-datatype parametric data, heavily skewed and multimodal distributions, and ten competing performance objectives.