Generative Models for Parameter Space Reduction applied to Reduced Order Modelling

TL;DR

This paper introduces a novel approach using Generative Models to reduce geometric parameters, thereby enhancing the accuracy and convergence of data-driven reduced order models and physics-informed neural networks for PDE solutions.

Contribution

It proposes integrating Generative Models with DROMs and PPINNs to effectively reduce parameter space and improve model performance in PDE problems involving complex geometries.

Findings

Enhanced accuracy of DROMs with generated geometries

Improved convergence of PPINNs through parameter reduction

Successful application to Poisson equation on Stanford Bunny domains

Abstract

Solving and optimising Partial Differential Equations (PDEs) in geometrically parameterised domains often requires iterative methods, leading to high computational and time complexities. One potential solution is to learn a direct mapping from the parameters to the PDE solution. Two prominent methods for this are Data-driven Non-Intrusive Reduced Order Models (DROMs) and Parametrised Physics Informed Neural Networks (PPINNs). However, their accuracy tends to degrade as the number of geometric parameters increases. To address this, we propose adopting Generative Models to create new geometries, effectively reducing the number of parameters, and improving the performance of DROMs and PPINNs. The first section briefly reviews the general theory of Generative Models and provides some examples, whereas the second focusses on their application to geometries with fixed or variable points, emphasising their integration with DROMs and PPINNs. DROMs trained on geometries generated by these models demonstrate enhanced accuracy due to reduced parameter dimensionality. For PPINNs, we introduce a methodology that leverages Generative Models to reduce the parameter dimensions and improve convergence. This approach is tested on a Poisson equation defined over deformed Stanford Bunny domains.