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Physics-based linear regression for high-dimensional forward uncertainty quantification
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DOI：10.1016/j.jcp.2024.113668.png)
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Abstract
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We introduce linear regression using physics-based basis functions optimized through the geometry of an inner product space. This method addresses the challenge of surrogate modeling with high-dimensional input, as the physics-based basis functions encode problem-specific information. We demonstrate the method using two proof-of-concept stochastic dynamic examples.
Keywords:
High-dimensional regression
Physics-based surrogate modeling
Uncertainty quantification
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