Towards Digital Twin of an In-situ Experiment: A Physics-enhanced Machine-Learning Framework for Inv...
Towards Digital Twin of an In-situ Experiment: A Physics-enhanced Machine-Learning Framework for Inverse Modelling of Mass Transport Processes
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Washington: American Chemical Society
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Language
English
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Washington: American Chemical Society
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As a prove of concept for experimental geochemistry, an advanced 3D numerical framework, here and after called Digital Twin (DT), of a diffusion experiment conducted at a synchrotron beamline, has been implemented using in-situ measurements data, physics-based modelling, a machine learning (ML) model, and parameter optimization module. The physics-...
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Towards Digital Twin of an In-situ Experiment: A Physics-enhanced Machine-Learning Framework for Inverse Modelling of Mass Transport Processes
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TN_cdi_proquest_journals_3123122262
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3123122262
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E-ISSN
2573-2293
DOI
10.26434/chemrxiv-2024-jf17j
