YoVDO

Machine Learning and Resampling for Stochastic Parameterization with Memory

Offered By: PCS Institute for Basic Science via YouTube

Tags

Machine Learning Courses Neural Networks Courses Climate System Courses Resampling Courses Curse of Dimensionality Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore machine learning and resampling techniques for stochastic parameterization with memory in this 25-minute conference talk. Delve into the world of multiscale dynamical systems, such as the climate system, and discover how data-based methods using machine learning are revolutionizing the parameterization of unresolved processes. Learn about the advantages of stochastic parameterization over deterministic approaches in accounting for uncertainty in small-scale to large-scale process feedback. Examine recent work on constructing data-based stochastic parameterizations with memory through resampling techniques, including binning and neural networks for probabilistic classification. Gain insights into overcoming the curse of dimensionality in long memory scenarios and evaluate the performance of these approaches on various test problems.

Syllabus

Daan Crommelin: Machine Learning and Resampling for Stochastic Parameterization with Memory


Taught by

PCS Institute for Basic Science

Related Courses

Dimensionality Reduction in Python
DataCamp
Sparse Nonlinear Dynamics Models with SINDy - The Library of Candidate Nonlinearities
Steve Brunton via YouTube
Overcoming the Curse of Dimensionality and Mode Collapse - Ke Li
Institute for Advanced Study via YouTube
Emergent Linguistic Structure in Deep Contextual Neural Word Representations - Chris Manning
Institute for Advanced Study via YouTube
Multilevel Weighted Least Squares Polynomial Approximation – Sören Wolfers, KAUST
Alan Turing Institute via YouTube