7 citations · 14 across the 5 of their papers we have counts for
9 papers
ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems
Xingjie Li, Fei Lu, Felix X. -F. Ye
Efficient simulation of SDEs is essential in many applications, particularly for ergodic systems that demand efficient simulation of both short-time dynamics and large-time statist…
Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles
Quanjun Lang, Fei Lu
We introduce a nonparametric algorithm to learn interaction kernels of mean-field equations for 1st-order systems of interacting particles. The data consist of discrete space-time…
Data-driven model reduction for stochastic Burgers equations
Fei Lu
We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by…
Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories
Fei Lu, Mauro Maggioni, Sui Tang
We consider stochastic systems of interacting particles or agents, with dynamics determined by an interaction kernel which only depends on pairwise distances. We study the problem…
On the identifiability of interaction functions in systems of interacting particles
Zhongyang Li, Fei Lu, Mauro Maggioni +2
We address a fundamental issue in the nonparametric inference for systems of interacting particles: the identifiability of the interaction functions. We prove that the interaction…
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Fei Lu, Mauro Maggioni, Sui Tang
Systems of interacting particles or agents have wide applications in many disciplines such as Physics, Chemistry, Biology and Economics. These systems are governed by interaction l…