Hyper-differential sensitivity analysis with respect to model discrepancy: Sequential optimal experimental design
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910098157207552 |
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| author | Madhavan, Madhusudan Hart, Joseph Waanders, Bart van Bloemen |
| author_facet | Madhavan, Madhusudan Hart, Joseph Waanders, Bart van Bloemen |
| contents | Large-scale optimization problems are ubiquitous in the physical sciences; yet, high-fidelity models can often be complex and computationally prohibitive for optimization. A practical alternative is to use a low-fidelity model to facilitate optimization. However, the discrepancy between the high- and low-fidelity models can lead to suboptimal solutions. To address this, we build on recent work in Hyper-Differential Sensitivity Analysis to leverage limited high-fidelity simulations to update the optimization solution. Our contributions in this article include: (i) incorporating pseudo-time continuation techniques to efficiently compute higher-accuracy optimal solution updates, and (ii) proposing a Bayesian framework for sequential data acquisition that strategically guides high-fidelity evaluations and reduces uncertainty in the model discrepancy estimation. Numerical results demonstrate that our framework delivers significant improvements to optimization solutions with only a few high-fidelity evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hyper-differential sensitivity analysis with respect to model discrepancy: Sequential optimal experimental design Madhavan, Madhusudan Hart, Joseph Waanders, Bart van Bloemen Numerical Analysis Large-scale optimization problems are ubiquitous in the physical sciences; yet, high-fidelity models can often be complex and computationally prohibitive for optimization. A practical alternative is to use a low-fidelity model to facilitate optimization. However, the discrepancy between the high- and low-fidelity models can lead to suboptimal solutions. To address this, we build on recent work in Hyper-Differential Sensitivity Analysis to leverage limited high-fidelity simulations to update the optimization solution. Our contributions in this article include: (i) incorporating pseudo-time continuation techniques to efficiently compute higher-accuracy optimal solution updates, and (ii) proposing a Bayesian framework for sequential data acquisition that strategically guides high-fidelity evaluations and reduces uncertainty in the model discrepancy estimation. Numerical results demonstrate that our framework delivers significant improvements to optimization solutions with only a few high-fidelity evaluations. |
| title | Hyper-differential sensitivity analysis with respect to model discrepancy: Sequential optimal experimental design |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2604.02253 |