Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …
climate information. Here we argue that now is the time to push the frontiers of machine …
Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
Interpretable machine learning for science with PySR and SymbolicRegression. jl
M Cranmer - arxiv preprint arxiv:2305.01582, 2023 - arxiv.org
PySR is an open-source library for practical symbolic regression, a type of machine learning
which aims to discover human-interpretable symbolic models. PySR was developed to …
which aims to discover human-interpretable symbolic models. PySR was developed to …
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
Machine learning for climate physics and simulations
We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …
learning (ML) and climate physics, highlighting the use of ML techniques, including …
[HTML][HTML] Machine learning for numerical weather and climate modelling: a review
CO de Burgh-Day… - Geoscientific Model …, 2023 - gmd.copernicus.org
Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …
modelling. Applications range from improved solvers and preconditioners, to …
Machine learning for the physics of climate
Climate science has been revolutionized by the combined effects of an exponential growth
in computing power, which has enabled more sophisticated and higher-resolution …
in computing power, which has enabled more sophisticated and higher-resolution …
Implementation and evaluation of a machine learned mesoscale eddy parameterization into a numerical ocean circulation model
We address the question of how to use a machine learned (ML) parameterization in a
general circulation model (GCM), and assess its performance both computationally and …
general circulation model (GCM), and assess its performance both computationally and …
Generative data‐driven approaches for stochastic subgrid parameterizations in an idealized ocean model
Subgrid parameterizations of mesoscale eddies continue to be in demand for climate
simulations. These subgrid parameterizations can be powerfully designed using physics …
simulations. These subgrid parameterizations can be powerfully designed using physics …
Data imbalance, uncertainty quantification, and transfer learning in data‐driven parameterizations: Lessons from the emulation of gravity wave momentum transport in …
Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale
parameterizations in weather and climate models. While NNs are powerful tools for learning …
parameterizations in weather and climate models. While NNs are powerful tools for learning …