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10 ways to apply machine learning in Earth & space sciences + Edge of chaos - C C - Jun 29, 2021
Edge of chaos for AI: network of random nanowires overcomes computational duality
https://www.scivillage.com/thread-10559-post-44469.html#pid44469
Ten ways to apply machine learning in Earth and space sciences
https://eos.org/opinions/ten-ways-to-apply-machine-learning-in-earth-and-space-sciences
INTRO: Machine learning is gaining popularity across scientific and technical fields, but it’s often not clear to researchers, especially young scientists, how they can apply these methods in their work.
Machine learning (ML), loosely defined as the “ability of computers to learn from data without being explicitly programmed,” has become tremendously popular in technical disciplines over the past decade or so, with applications including complex game playing and image recognition carried out with superhuman capabilities. The Earth and space sciences (ESS) community has also increasingly adopted ML approaches to help tackle pressing questions and unwieldy data sets. From 2009 to 2019, for example, the number of studies involving ML published in AGU journals approximately doubled.
In many ways, ESS present ideal use cases for ML applications because the problems being addressed—like climate change, weather forecasting, and natural hazards assessment—are globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so... (MORE - details)
COVERED: The Tools of the Trade ..... Applications in Earth and Space Sciences ..... Pattern Identification and Clustering ..... Time Series and Spatiotemporal Prediction ..... Emulators and Surrogates ..... Boundary or Driving Conditions ..... Interpretability and Knowledge Discovery ..... Accelerating Inversions ..... Creating High-Resolution Global Data Sets ..... Uncertainty Quantification ..... Physics-Informed Neural Networks ..... Finding and Solving Governing Equations ..... Addressing Urgent Challenges