Using Machine Learning for Model Physics: an Overview
In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningRelationSimilar Papers 제목 키워드 기반
Machine learning in physics: a short guide
Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts o…
Causal Inferenceregressionreinforcement-learningReinforcement Learning+1Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and…
Physics-informed machine learningMachine Learning for Anomaly Detection in Particle Physics
The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phen…
Anomaly DetectionIntegrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine l…
BIG-bench Machine LearningUncertainty in Physics and AI: Taxonomy, Quantification, and Validation
Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a structured overview of uncertainty quan…