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We introduce two approximations that make this framework practical for advanced material testing with expensive forward models and high-dimensional data: (i) a Gaussian approximation of the expected information gain, and (ii) a surrogate approximation of the Fisher information matrix. The former enables efficient design optimization and interpretation, while the latter extends this approach to batched design optimization by amortizing the cost of repeated utility evaluations. Our numerical studies of uniaxial tests for viscoelastic solids show that optimized specimen geometries and loading paths yield image and force data that significantly improve parameter identifiability relative to random designs, especially for parameters associated with memory effects.
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Numerical Analysis (math.NA); Computational Physics (physics.comp-ph); Computation (stat.CO) |
| Cite as: | arXiv:2603.12365 [cond-mat.mtrl-sci] |
| (or arXiv:2603.12365v2 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2603.12365 arXiv-issued DOI via DataCite |
|
| Journal reference: | Computer Methods in Applied Mechanics and Engineering, Volume 457, 2026, 119022, ISSN 0045-7825 |
| Related DOI: | https://doi.org/10.1016/j.cma.2026.119022
DOI(s) linking to related resources |
From: Lianghao Cao [view email]
[v1]
Thu, 12 Mar 2026 18:33:06 UTC (12,668 KB)
[v2]
Sun, 26 Apr 2026 01:09:05 UTC (12,781 KB)
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