most citedDirected Chain Generative Adversarial Networks

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

math.AP20231 cited

On the Long-time Dynamics and Ergodicity of the Stochastic Nernst-Planck-Navier-Stokes System

Elie Abdo, Ruimeng Hu, Quyuan Lin

We consider an electrodiffusion model that describes the intricate interplay of multiple ionic species with a two-dimensional, incompressible, viscous fluid subjected to stochastic…

econ.GN20231 cited

A Deep Learning Analysis of Climate Change, Innovation, and Uncertainty

Michael Barnett, William Brock, Lars Peter Hansen +2

We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production…

math.OC20231 cited

Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms

Robert Balkin, Hector D. Ceniceros, Ruimeng Hu

In this paper, we propose a numerical methodology for finding the closed-loop Nash equilibrium of stochastic delay differential games through deep learning. These games are prevale…

cs.LG20231 cited

Directed Chain Generative Adversarial Networks

Ming Min, Ruimeng Hu, Tomoyuki Ichiba

Real-world data can be multimodal distributed, e.g., data describing the opinion divergence in a community, the interspike interval distribution of neurons, and the oscillators nat…

math.OC2022

Pandemic Control, Game Theory and Machine Learning

Yao Xuan, Robert Balkin, Jiequn Han +2

Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus…