12 papers
A Kernel Formula for Kinetic Fokker-Planck Equations
Fuqun Han, Wuchen Li
We derive an explicit one-step kernel formula for the kinetic Fokker-Planck equation. The construction begins with a reference dynamics admitting an explicit transition kernel and…
A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
Shu Liu, Stanley Osher, Wuchen Li
We propose a scalable preconditioned primal-dual hybrid gradient algorithm for solving partial differential equations (PDEs). We multiply the PDE with a dual test function to obtai…
Preconditioned Regularized Wasserstein Proximal Sampling
Hong Ye Tan, Stanley Osher, Wuchen Li
We consider sampling from a Gibbs distribution by evolving finitely many particles. We propose a preconditioned version of a recently proposed noise-free sampling method, governed…
Deep Kinetic JKO schemes for Vlasov-Fokker-Planck Equations
Wonjun Lee, Li Wang, Wuchen Li
We introduce a deep neural network-based numerical method for solving kinetic Fokker Planck equations, including both linear and nonlinear cases. Building upon the conservative dis…
Accelerated Markov Chain Monte Carlo Algorithms on Discrete States
Bohan Zhou, Shu Liu, Xinzhe Zuo +1
We propose a class of discrete state sampling algorithms based on Nesterov's accelerated gradient method, which extends the classical Metropolis-Hastings (MH) algorithm. The evolut…
Accelerated Regularized Wasserstein Proximal Sampling Algorithms
Hong Ye Tan, Stanley Osher, Wuchen Li
We consider sampling from a Gibbs distribution by evolving a finite number of particles using a particular score estimator rather than Brownian motion. To accelerate the particles,…