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Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization — arXiv:2609.02126

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.02126
License (as archived): CC0-1.0
Archived: 2026-10-17T04:59:25.450Z
SHA-256 of the archived text: 736bf84b3c93cbc0dfe3c10caf4fb07e96a879b1f3583c058bcea0fb226fe83f
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How to cite

Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization — arXiv:2609.02126 — arXiv (en), https://arxiv.org/abs/2609.02126 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd31d9787253 (Metraipolis archive dc_bd31d9787253, sha256 736bf84b3c93cbc0dfe3c10caf4fb07e96a879b1f3583c058bcea0fb226fe83f)

BibTeX

@misc{dcbd31d9787253,
  title = {Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization — arXiv:2609.02126},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.02126},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd31d9787253; sha256 736bf84b3c93cbc0dfe3c10caf4fb07e96a879b1f3583c058bcea0fb226fe83f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.02126. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Estimating physical parameters from scientific images is a common inverse problem in materials characterization that often relies on expensive physics-based simulations. In electron microscopy, specimen thickness and crystal mistilt are critical parameters that govern how electrons scatter through the sample, and therefore the accuracy of any atomic-scale structure recovered from it. They are commonly inferred by matching experimental position-averaged convergent-beam electron diffraction (PACBED) patterns to simulated ones, but grid searches scale poorly and neural-network methods require extensive pretraining that may not transfer to new conditions. Here, we propose scalable Bayesian optimization of composite functions (SBOCF), a simulation-efficient method that exploits the known composite structure of the image-matching objective and the intermediate information contained in simulated images. By representing PACBED images with patch-level summaries and two correction terms, SBOCF preserves the original pixel-wise objective while reducing the number of modeled outputs from 24,649 to 11. Under a budget of 50 simulator evaluations, SBOCF outperformed standard Bayesian optimization with expected improvement on synthetic SrTiO3 benchmarks with thick and thin specimens, reducing the median final SSE by up to 290x in the thick-sample case. On experimental data, SBOCF produced parameter estimates consistent with previously reported values without task-specific pretraining. For a simulated mistilted specimen, using the SBOCF estimates in a downstream ptychographic reconstruction recovered sharp atoms that were otherwise blurred. These results establish SBOCF as a promising approach for inverse problems involving expensive simulators and high-dimensional structured outputs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-03 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Materials Science (cond-mat.mtrl-sci).
Pagina del lavoro: https://arxiv.org/abs/2609.02126 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T04:09:43.311Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-03?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).