activity
20172026
most citedSolving parametric PDE problems with artificial neural networks

195 citations · 196 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

A Mathematical Introduction to Diffusion Models

Jianfeng Lu

These notes give a proof-oriented introduction to diffusion models from the viewpoint of sampling, tracing a single arc from classical sampling dynamics to modern diffusion sampler…

math.AP2026

A sharp hypocoercive entropy decay estimate for underdamped Langevin dynamics

Jianfeng Lu

We study the underdamped Langevin dynamics with invariant measure . Assume tha…

math.OC2026

Mirror Descent for Deterministic Optimal Control

Ye Feng, Jianfeng Lu

We study an explicit mirror-descent method for finite-horizon deterministic optimal control problems. The method is motivated by Pontryagin's maximum principle: at each iteration,…

stat.ML2025

Error Analysis of Generalized Langevin Equations with Approximated Memory Kernels

Quanjun Lang, Jianfeng Lu

We analyze prediction error in stochastic dynamical systems with memory, focusing on generalized Langevin equations (GLEs) formulated as stochastic Volterra equations. We establish…

quant-ph2025

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing

Quanjun Lang, Jianfeng Lu

Quantum superoperator learning is a pivotal task in quantum information science, enabling accurate reconstruction of unknown quantum operations from measurement data. We propose a…

stat.ML2024★ 1 cited

Learning Memory Kernels in Generalized Langevin Equations

Quanjun Lang, Jianfeng Lu

We introduce a novel approach for learning memory kernels in Generalized Langevin Equations. This approach initially utilizes a regularized Prony method to estimate correlation fun…

math.NA2017★ 195 cited

Solving parametric PDE problems with artificial neural networks

Yuehaw Khoo, Jianfeng Lu, Lexing Ying

The curse of dimensionality is commonly encountered in numerical partial differential equations (PDE), especially when uncertainties have to be modeled into the equations as random…