Elastic Multi-Fidelity Bayesian Model Calibration

2026-09-22

AI Summary

本文提出弹性多保真贝叶斯模型校准方法，通过将高低保真度的功能输出对齐到共同参考，解决模拟器与实验数据间的相位和幅度差异问题。

Abstract

Bayesian calibration of functional-output computer models typically relies on dimension reduction techniques, such as functional principal component analysis, which assume that differences among simulator realizations arise only from amplitude variation. When simulator output also exhibits phase variation such as shifts in the timing or location of key features, this assumption is violated. Recent work has addressed this issue through elastic calibration, which aligns functional computer model realizations with observed experimental data prior to dimension reduction. Separately, multi-fidelity methods reduce the cost of calibration by supplementing a small number of expensive high-fidelity simulator runs with a larger ensemble of cheap low-fidelity runs. This is typically done through either a mapping strategy, which corrects low-fidelity predictions toward high-fidelity output, or a fusion strategy, which builds a shared basis across both fidelities. This paper combines these two lines of work, introducing elastic multi-fidelity Bayesian model calibration, which aligns high- and low-fidelity functional output to a common reference before applying multi-fidelity mapping or fusion. On a synthetic two-dimensional calibration problem and a dynamic material properties equation-of-state problem, both elastic multi-fidelity strategies match or improve on the leave-one-out predictive accuracy of a mono-fidelity elastic emulator, with the fusion approach achieving the lowest error. Both strategies also produce tighter calibrated posteriors than the mono-fidelity baseline, with the fusion approach providing the best coverage and parameter estimates closest to the true values.

Problem

• Elastic Calibration
• Multi-fidelity Methods
• Functional Principal Component Analysis
• Phase Variation

Innovation

• Elastic Multi-Fidelity
• Bayesian Model Calibration
• Dimension Reduction
• Functional Principal Component Analysis
• Multi-fidelity Methods