Article2013

Model Bias Characterization in the Design Space under Uncertainty

22 citations0/0 files storedInternational Journal of Performability Engineering

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Probabilistic and Robust Engineering Design Optimal Experimental Design Methods Advanced Multi-Objective Optimization Algorithms

Abstract

This paper presents an approach to validate computational models in the design space under uncertainty. The basic idea is to first characterize the model bias; then correct the original model prediction by adding the characterized model bias in the design space. Particularly, a two-step calibration procedure is proposed and the model bias at each design configuration is approximated using the Maximum Entropy Principle (MEP) method. With the characterized model bias at several design configurations, response surface of the model bias is finally constructed to approximate the model bias at any new design configurations. Two examples including a modified vehicle side impact problem and a thermal problem are used to demonstrate the feasibility of the proposed approach.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 22 citations exist upstream.

References

1996Response Surface Methodology: Process and Product Optimization Using Designed Experiments 2004Reliability-based design optimization for crashworthiness of vehicle side impact 2004Concepts of Model Verification and Validation 2008Eigenvector dimension reduction (EDR) method for sensitivity-free probability analysis 2008A better understanding of model updating strategies in validating engineering models 2007A Design-Driven Validation Approach Using Bayesian Prediction Models 1999Statistical Validation of Engineering and Scientific Models: Background 2010A hierarchical framework for statistical model calibration in engineering product development

Cited by

2018Active Resource Allocation for Reliability Analysis With Model Bias Correction 2016Reliability-Based Design Optimization Using Confidence-Based Model Validation for Insufficient Experimental Data 2020Heterogeneous uncertainty quantification using Bayesian inference for simulation-based design optimization 2017Reliability-Based Design Optimization Using Confidence-Based Model Validation for Insufficient Experimental Data

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.