17 citations · 35 across the 7 of their papers we have counts for
7 papers
Reduced order model for parametric Boltzmann equation and its application to inverse problems
Shanyin Tong, Jingwei Hu, Fengyan Li +3
The Boltzmann equation plays an important role in modeling mesoscopic behavior in a wide range of scientific and engineering applications. However, its numerical solution is comput…
Structure-Preserving Reduced-Order Modeling via Low-Rank Transport Signatures
Jiajia Yu, Jingwei Hu, Fengyan Li +3
Parametrized PDEs with density-valued solutions are often difficult to approximate with classical linear reduced-order models, especially in transport-dominated regimes. We introdu…
Zeroth-order Stochastic Cubic Newton Method Revisited
Yu Liu, Weibin Peng, Tianyu Wang +1
This paper studies stochastic minimization of a finite-sum loss . In many real-world scenarios, the Hessian matrix of s…
Zeroth-order Low-rank Hessian Estimation via Matrix Recovery
Tianyu Wang, Zicheng Wang, Jiajia Yu
A zeroth-order Hessian estimator aims to recover the Hessian matrix of an objective function at any given point, using minimal finite-difference computations. This paper studies ze…
A Bilevel Optimization Method for Inverse Mean-Field Games
Jiajia Yu, Quan Xiao, Tianyi Chen +1
In this paper, we introduce a bilevel optimization framework for addressing inverse mean-field games, alongside an exploration of numerical methods tailored for this bilevel proble…
Bridging Mean-Field Games and Normalizing Flows with Trajectory Regularization
Han Huang, Jiajia Yu, Jie Chen +1
Mean-field games (MFGs) are a modeling framework for systems with a large number of interacting agents. They have applications in economics, finance, and game theory. Normalizing f…
Computational Mean-field Games on Manifolds
Jiajia Yu, Rongjie Lai, Wuchen Li +1
Conventional Mean-field games/control study the behavior of a large number of rational agents moving in the Euclidean spaces. In this work, we explore the mean-field games on Riema…