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Published online by Cambridge University Press: 23 March 2023
Three areas where machine learning (ML) and physics have been merging: (a) Physical models can have computationally expensive components replaced by inexpensive ML models, giving rise to hybrid models. (b) In physics-informed machine learning, ML models can be solved satisfying the laws of physics (e.g. conservation of energy, mass, etc.) either approximately or exactly. (c) In forecasting, ML models can be combined with numerical/dynamical models under data assimilation.
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