most citedA Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)

3 citations · 8 across the 5 of their papers we have counts for

collaborators

6 papers

math.DS2024

Data-driven cold starting of good reservoirs

Lyudmila Grigoryeva, Boumediene Hamzi, Felix P. Kemeth +4

Using short histories of observations from a dynamical system, a workflow for the post-training initialization of reservoir computing systems is described. This strategy is called…

physics.ao-ph2023

Tipping points in overturning circulation mediated by ocean mixing and the configuration and magnitude of the hydrological cycle: A simple model

Anand Gnanadesikan, Gianluca Fabiani, Jingwen Liu +7

The current configuration of the ocean overturning involves upwelling predominantly in the Southern Ocean and sinking predominantly in the Atlantic basin. The reasons for this rema…

cs.LG20233 cited

A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)

Jennifer Sleeman, David Chung, Anand Gnanadesikan +10

We propose a new Tipping Point Generative Adversarial Network (TIP-GAN) for better characterizing potential climate tipping points in Earth system models. We describe an adversaria…

cs.AI20232 cited

Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points

Jennifer Sleeman, David Chung, Chace Ashcraft +10

We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology bas…

math.NA20233 cited

Accurate and efficient multiscale simulation of a heterogeneous elastic beam via computation on small sparse patches

A. J. Roberts, Thien Tran-Duc, J. E. Bunder +1

Modern `smart' materials have complex microscale structure, often with unknown macroscale closure. The Equation-Free Patch Scheme empowers us to non-intrusively, efficiently, and a…

stat.ML2022

Unsupervised learning of observation functions in state-space models by nonparametric moment methods

Qingci An, Yannis Kevrekidis, Fei Lu +1

We investigate the unsupervised learning of non-invertible observation functions in nonlinear state-space models. Assuming abundant data of the observation process along with the d…