Physics-informed deep learning for prediction of CO2 storage site response
Presented at the DOE-NETL 2020 Virtual Meeting - SMART Initiative Annual Review Meeting November 2-3, 2020, 11/2
Shokouhi, Parisa↗
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Results for “Physics Informed”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Physics-informed Data-driven Machine Learning Approach for Mesoscale Materials Science
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Pokharel, Reeju↗
Nonlocal Physics-Informed Neural Networks: A unified theoretical and computational framework for nonlocal models.
Abstract not provided.
D'Elia, Marta↗
Unsupervised and Physics-Informed Machine learning in Geosciences
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Earth Sciences↗
Physics-informed graph neural nets (pigNNS) A unification of NN architectures with mimetic PDE discretization.
Abstract not provided.
Trask, Nathaniel Albert↗
Physics-informed graph neural netsA unification of NN architectures with mimetic PDE discretization.
Abstract not provided.
Trask, Nathaniel Albert↗
Applications of physics-informed scientific machine learning in subsurface science: A survey.
Abstract not provided.
Sun, Alexander Y↗
Physics-Informed Graph Neural Network for Circuit Compact Model Development.
Abstract not provided.
Gao, Xujiao↗
Physics-informed deep learning with uncertainty quantification for weapons radiography, NLV-003-21, Year 1 of 2
Presentation to be presented during the Site-Directed Research and Development (SDRD) Program FY 2021 Review Meeting (webex), September 22–23, 2021.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Physics-Informed Graph Neural Network for Circuit Compact Model Development.
Abstract not provided.
Gao, Xujiao↗
Indirect measurement of underground facility expansion/extent through the multi-modal use of cyber metadata (traceroutes) and physics-informed models applied to pre-post digital surface mappingNLV-012-21, Year 1 of 3
Presentation to be presented during the Site-Directed Research and Development (SDRD) program FY 2021 review meeting (Webex), September 22–23, 2021.
Champion, Daniel↗
Tracking Material Transfers at a Nuclear Facility with Physics-Informed Machine Learning and Data Fusion
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Dayman, Ken J.↗
(U) Physics-informed deep learning with uncertainty quantification for weapons radiography
This is my 2-page end of year report for my SDRD which will continue in FY22.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
mystic: physics-informed learning and automated design/control under uncertainty
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McKerns, Michael↗
Physics-informed, empirically constrained machine learning for designing Fe-9Cr alloys
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. It was demonstrated that the domain knowledge-based empirical constraints not only inhibit overfitting but also allow training more accurate and reliable ML models with improved transparency of the output interpretation. In this study, alloy tensile properties were interpreted with three competing virtual-microstructure models.</span>
Romanov, Vyacheslav↗
Physics-Informed Machine Learning for Fault-Leakage Reduced-Order Model Development
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Earth Sciences↗
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation.
Abstract not provided.
Kim, Jungeun↗