Model discrepancy calibration across experimental settings

Maupin Kathryn A.; Swiler Laura P.

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Model discrepancy calibration across experimental settings

机译：实验设置模型差异校准

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摘要

Despite continuing advances in the reliability of computational modeling and simulation, model inadequacy remains a pervasive concern across scientific disciplines. Further challenges are introduced into the already complex problem of "correcting" an inadequate model when experimental data is collected at varying experimental settings. This paper introduces a general approach to calibrating a model discrepancy function when the model is expected to perform for multiple experimental configurations and give predictions as a function of temporal and/or spatial coordinates.

机译：尽管在计算建模和仿真的可靠性继续前进，但模型不足仍然是科学学科的普遍关注。在不同实验设置收集实验数据时，将进一步的挑战引入已经复杂的“校正”模型中的内容不足的问题。本文介绍了一种校准模型差异函数的一般方法，当预期为多种实验配置执行模型，并作为时间和/或空间坐标的函数给出预测。

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来源
《Reliability Engineering & System Safety》 |2020年第8期|106818.1-106818.10|共10页
作者
Maupin Kathryn A.; Swiler Laura P.;
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作者单位

Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA;

Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Computational modeling; Model inadequacy; Model form error; Model discrepancy; Inverse problems; Bayes' Rule;

机译：计算建模;模型不足;模型形式错误;模型差异;逆问题;贝叶斯的规则;

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Model discrepancy calibration across experimental settings

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