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A systematic approach for matching simulated and experimental

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Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns

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Scheme of the experimental workflow. (A) Sample collection (B) Sample

Integration of machine learning with neutron scattering for the Hamiltonian tuning of spin ice under pressure

How to learn from inconsistencies: Integrating molecular simulations with experimental data - ScienceDirect

Frontiers Evaluation of Methods for the Extraction of Spatial Muscle Synergies

A systematic approach for matching simulated and experimental

Duckweed Assisted Sediment Microbial Fuel Cell for Powering Small

Frontiers LANet: Stereo matching network based on linear-attention mechanism for depth estimation optimization in 3D reconstruction of inter-forest scene

Methods for Grid Generation in CFD Simulations, System Analysis Blog

Societies, Free Full-Text