Designing Air Flow with Surrogate-assisted Phenotypic Niching

TL;DR

This paper introduces surrogate-assisted phenotypic niching, a method that efficiently discovers diverse behaviors in complex fluid dynamics problems using surrogate models and GPU simulations, aiding domain understanding.

Contribution

It presents a novel quality diversity algorithm that models phenotypic features data-drivenly, reducing computational costs in fluid dynamics optimization.

Findings

Successfully modeled air flow behaviors with surrogate models

Reduced number of simulations needed for diverse solutions

Enhanced understanding of complex fluid behaviors

Abstract

In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself. But what if we could quickly understand the complex behaviors that can emerge in said domains instead? We introduce surrogate-assisted phenotypic niching, a quality diversity algorithm which allows to discover a large, diverse set of behaviors by using computationally expensive phenotypic features. In this work we discover the types of air flow in a 2D fluid dynamics optimization problem. A fast GPU-based fluid dynamics solver is used in conjunction with surrogate models to accurately predict fluid characteristics from the shapes that produce the air flow. We show that these features can be modeled in a data-driven way while sampling to improve performance, rather than explicitly sampling to improve feature models. Our method can reduce the need to run an infeasibly large set of simulations while still being able to design a large diversity of air flows and the shapes that cause them. Discovering diversity of behaviors helps engineers to better understand expensive domains and their solutions.