SOTAVerified

Physics-informed machine learning

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

Papers

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Showing 111–120 of 192 papers

TitleStatusHype
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning—0
Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning—0
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient—0
Spectrally Informed Learning of Fluid Flows—0
Structural Constraints for Physics-augmented Learning—0
Tensor Basis Gaussian Process Models of Hyperelastic Materials—0
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
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