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Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

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 ext

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