SOTAVerified

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
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks—0
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions—0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces—0
Physics-Informed Machine Learning for Efficient Reconfigurable Intelligent Surface Design—0
Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Towards Physically Interpretable World Models: Meaningful Weakly Supervised Representations for Visual Trajectory Prediction—0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity FactorsCode0
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