# Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization

arXiv: https://arxiv.org/abs/2308.04051 · HTML: https://mlsanity.com/paper/2308.04051/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 86 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Airfoil Design Parameterization and Optimization using B'ezier Generative Adversarial Networks](https://mlsanity.com/paper/2006.12496/) (2020)
- [ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model](https://mlsanity.com/paper/2305.00210/) (2023)
- [Parametric Model Embedding](https://mlsanity.com/paper/2204.05371/) (2022)
- [A Taxonomy of Constraints in Simulation-Based Optimization](https://mlsanity.com/paper/1505.07881/) (2015)
- [Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks](https://mlsanity.com/paper/2307.05639/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization](https://mlsanity.com/paper/2405.13944/) (2024)

## Abstract (arXiv)

Our work presents a novel approach to shape optimization, with the twofold objective to improve the efficiency of global optimization algorithms while promoting the generation of high-quality designs during the optimization process free of geometrical anomalies. This is accomplished by reducing the number of the original design variables defining a new reduced subspace where the geometrical variance is maximized and modeling the underlying generative process of the data via probabilistic linear latent variable models such as factor analysis and probabilistic principal component analysis. We show that the data follows approximately a Gaussian distribution when the shape modification method is linear and the design variables are sampled uniformly at random, due to the direct application of the central limit theorem. The degree of anomalousness is measured in terms of Mahalanobis distance, and the paper demonstrates that abnormal designs tend to exhibit a high value of this metric. This enables the definition of a new optimization model where anomalous geometries are penalized and consequently avoided during the optimization loop. The procedure is demonstrated for hull shape optimization of the DTMB 5415 model, extensively used as an international benchmark for shape optimization problems. The global optimization routine is carried out using Bayesian optimization and the DIRECT algorithm. From the numerical results, the new framework improves the convergence of global optimization algorithms, while only designs with high-quality geometrical features are generated through the optimization routine thereby avoiding the wastage of precious computationally expensive simulations.