Overview
Jianfeng Lu is an applied mathematician interested in mathematical analysis and algorithm development for problems from computational physics, theoretical chemistry, materials science, machine learning, and other related fields.
More specifically, his current research focuses include:
High dimensional PDEs; generative models and sampling methods; control and reinforcement learning; electronic structure and many body problems; quantum molecular dynamics; multiscale modeling and analysis.
More specifically, his current research focuses include:
High dimensional PDEs; generative models and sampling methods; control and reinforcement learning; electronic structure and many body problems; quantum molecular dynamics; multiscale modeling and analysis.
Current Appointments & Affiliations
James B. Duke Distinguished Professor of Mathematics
·
2024 - Present
Mathematics,
Trinity College of Arts & Sciences
Professor of Mathematics
·
2020 - Present
Mathematics,
Trinity College of Arts & Sciences
Associate Professor of Chemistry
·
2016 - Present
Chemistry,
Trinity College of Arts & Sciences
Professor of Physics
·
2023 - Present
Physics,
Trinity College of Arts & Sciences
Recent Publications
Quantum circuit for non-unitary linear transformation of basis sets
Journal Article Npj Quantum Information · December 1, 2025 This paper presents a novel approach for implementing non-unitary basis transformations on quantum computational platforms, extending beyond conventional unitary methods. By integrating Singular Value Decomposition (SVD), the method achieves operational de ... Full text CiteFULLY DISCRETIZED SOBOLEV GRADIENT FLOW FOR THE GROSS-PITAEVSKII EIGENVALUE PROBLEM
Journal Article Mathematics of Computation · November 1, 2025 This paper studies the numerical approximation of the ground state of the Gross-Pitaevskii (GP) eigenvalue problem with a fully discretized Sobolev gradient flow induced by the H1 norm. For the spatial discretization, we consider the finite element method ... Full text CiteRegularized Stein Variational Gradient Flow
Journal Article Foundations of Computational Mathematics · August 1, 2025 The stein variational gradient descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational Gradient Flow) only prov ... Full text CiteRecent Grants
Collaborative Research: RI: Medium: Machine learning for PDEs, and with PDEs
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2024 - 20282026 Gene Golub SIAM Summer School
ConferenceKey Faculty · Awarded by Society for Industrial and Applied Mathematics · 2025 - 2027NRT-HDR: Harnessing AI for Autonomous Material Design
Inst. Training Prgm or CMEParticipants · Awarded by National Science Foundation · 2020 - 2026View All Grants
Education, Training & Certifications
Princeton University ·
2009
Ph.D.