Approximated Computation of Belief Functions for Robust Design Optimization

2012/07/14 by Vasile, Massimiliano, Minisci, Edmondo, Wijnands, Quirien
#Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Finance #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Probability (math.PR) #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.1207.3442

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.

Related

• Survey of multifidelity methods in uncertainty propagation, inference,\n and optimization
• Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
• The Optimal Uncertainty Algorithm in the Mystic Framework
• The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations
• A Methodology to Identify Physical or Computational Experiment Conditions for Uncertainty Mitigation