A Data-driven Approach to Risk-aware Robust Design

TL;DR

This paper introduces a data-driven, risk-aware robust design framework that balances optimality and robustness by considering perturbed scenarios and outlier removal, demonstrated through aeroelastic wing design.

Contribution

It presents a novel formulation for risk-averse and risk-agnostic robust design that incorporates scenario perturbations and outlier elimination to improve reliability and prevent overfitting.

Findings

The approach effectively balances robustness and optimality.

It reduces overfitting by considering scenario perturbations.

Demonstrated on aeroelastic wing design with promising results.

Abstract

This paper proposes risk-averse and risk-agnostic formulations to robust design in which solutions that satisfy the system requirements for a set of scenarios are pursued. These scenarios, which correspond to realizations of uncertain parameters or varying operating conditions, can be obtained either experimentally or synthetically. The proposed designs are made robust to variations in the training data by considering perturbed scenarios. This practice allows accounting for error and uncertainty in the measurements, thereby preventing data overfitting. Furthermore, we use relaxation to trade-off a lower optimal objective value against lesser robustness to uncertainty. This is attained by eliminating a given number of optimally chosen outliers from the dataset, and by allowing the perturbed scenarios to violate the requirements with an acceptably small probability. For instance, we can seek a design that satisfies the requirements for as many perturbed scenarios as possible, or pursue a riskier design that attains a lower objective value in exchange for a few scenarios violating the requirements. These ideas are illustrated by considering the design of an aeroelastic wing.