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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 31–40 of 192 papers

TitleStatusHype
Toward Physics-Informed Machine Learning for Data Center Operations: A Tropical Case Study—0
Scientific machine learning in Hydrology: a unified perspective—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning ApproachCode0
LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSECode0
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers—0
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis—0
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design—0
A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution AnalysisCode0
Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning—0
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