Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913157514002432 |
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| author | Scheffels, Jakob Qian, Elizabeth Papaioannou, Iason Ullmann, Elisabeth |
| author_facet | Scheffels, Jakob Qian, Elizabeth Papaioannou, Iason Ullmann, Elisabeth |
| contents | Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engineering applications, but computational solution of Bayesian inverse problems is often expensive because standard solution approaches require many evaluations of the forward model mapping the parameter value to predicted observations. In many settings, this forward model is expensive because it requires the solution of a high-dimensional discretization of a partial differential equation. However, Bayesian inverse problems often exhibit low-dimensional structure because the available data are primarily informative (relative to the prior) in a low-dimensional subspace, sometimes called the likelihood-informed subspace (LIS). This paper proposes a new projection-based model reduction method for static linear systems that exploits this low-dimensional structure in the setting where the unknown parameter is the right-hand-side forcing, giving rise to a linear inverse problem. The proposed method projects the governing partial differential equation onto the likelihood-informed subspace, yielding a computationally efficient reduced model that can be used to accelerate the solution of the inverse problem and subsequent downstream computations. Numerical experiments on two structural engineering model problems demonstrate that the proposed approach can successfully exploit the intrinsic low-dimensionality of the problem, obtaining relative errors in O(10^{-10}) in the inverse problem solution with a 10x and 100x lower-dimensional model, respectively. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_07950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads Scheffels, Jakob Qian, Elizabeth Papaioannou, Iason Ullmann, Elisabeth Numerical Analysis Bayesian inverse problems use data to update a prior probability distribution on uncertain parameter values to a posterior distribution. Such problems arise in many structural engineering applications, but computational solution of Bayesian inverse problems is often expensive because standard solution approaches require many evaluations of the forward model mapping the parameter value to predicted observations. In many settings, this forward model is expensive because it requires the solution of a high-dimensional discretization of a partial differential equation. However, Bayesian inverse problems often exhibit low-dimensional structure because the available data are primarily informative (relative to the prior) in a low-dimensional subspace, sometimes called the likelihood-informed subspace (LIS). This paper proposes a new projection-based model reduction method for static linear systems that exploits this low-dimensional structure in the setting where the unknown parameter is the right-hand-side forcing, giving rise to a linear inverse problem. The proposed method projects the governing partial differential equation onto the likelihood-informed subspace, yielding a computationally efficient reduced model that can be used to accelerate the solution of the inverse problem and subsequent downstream computations. Numerical experiments on two structural engineering model problems demonstrate that the proposed approach can successfully exploit the intrinsic low-dimensionality of the problem, obtaining relative errors in O(10^{-10}) in the inverse problem solution with a 10x and 100x lower-dimensional model, respectively. |
| title | Likelihood-informed Model Reduction for Bayesian Inference of Static Structural Loads |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2510.07950 |