Approximated Computation of Belief Functions for Robust Design Optimization

TL;DR

This paper introduces approximation techniques to efficiently compute Belief and Plausibility in Evidence Theory, enabling robust design optimization with reduced computational costs, demonstrated through test cases and a spacecraft system example.

Contribution

It proposes novel approximation methods for Belief and Plausibility calculations in Evidence Theory, significantly reducing computational effort in robust design optimization.

Findings

Approximation methods scale well with problem dimension.

Techniques reduce computation time to a fraction of exact calculations.

Successful application to spacecraft system design example.

Abstract

This paper presents some ideas to reduce the computational cost of evidence-based robust design optimization. Evidence Theory crystallizes both the aleatory and epistemic uncertainties in the design parameters, providing two quantitative measures, Belief and Plausibility, of the credibility of the computed value of the design budgets. The paper proposes some techniques to compute an approximation of Belief and Plausibility at a cost that is a fraction of the one required for an accurate calculation of the two values. Some simple test cases will show how the proposed techniques scale with the dimension of the problem. Finally a simple example of spacecraft system design is presented.