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Found 8 papers, 5 papers with code
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data
This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.
Improved Active Learning via Dependent Leverage Score Sampling
We show how to obtain improved active learning methods in the agnostic (adversarial noise) setting by combining marginal leverage score sampling with non-independent sampling strategies that promote spatial coverage.
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
Understanding dynamics in complex systems is challenging because there are many degrees of freedom, and those that are most important for describing events of interest are often not obvious.
Understanding and eliminating spurious modes in variational Monte Carlo using collective variables
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years.
Learning forecasts of rare stratospheric transitions from short simulations
We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics.
Atmospheric and Oceanic Physics Dynamical Systems Data Analysis, Statistics and Probability
A metric on directed graphs and Markov chains based on hitting probabilities
The shortest-path, commute time, and diffusion distances on undirected graphs have been widely employed in applications such as dimensionality reduction, link prediction, and trip planning.
Umbrella sampling: a powerful method to sample tails of distributions
We present the umbrella sampling (US) technique and show that it can be used to sample extremely low probability areas of the posterior distribution that may be required in statistical analyses of data.
Instrumentation and Methods for Astrophysics
Ensemble preconditioning for Markov chain Monte Carlo simulation
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas.
