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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 121–130 of 192 papers

TitleStatusHype
Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy—0
Toward Physics-Informed Machine Learning for Data Center Operations: A Tropical Case Study—0
Towards Physically Interpretable World Models: Meaningful Weakly Supervised Representations for Visual Trajectory Prediction—0
Transcriptome and Redox Proteome Reveal Temporal Scales of Carbon Metabolism Regulation in Model Cyanobacteria Under Light Disturbance—0
Transforming Future Data Center Operations and Management via Physical AI—0
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions—0
Universal Battery Performance and Degradation Model for Electric Aircraft—0
Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models—0
Valuation of Public Bus Electrification with Open Data—0
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning—0
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