- Professor of Mathematics
- Associate Professor of Physics (Secondary)
- Associate Professor of Chemistry (Secondary)
- Associate Professor in Physics (Secondary)
Research Areas and Keywords
electronic structure models, calculus of variations, semiclassical analysis
electronic structure models, multiscale modeling and simulations, numerical analysis, rare events simulation, computational physics, time-frequency analysis, fast algorithms, stochastic numerical methods, kinetic equations, nonlinear Schrodinger equations, quantum chemistry, computational statistical mechanics, optimization, high frequency wave propagation
electronic structure models, quantum chemistry, kinetic theory, quantum information
PDE & Dynamical Systems
multiscale modeling and simulations, numerical analysis, calculus of variations, kinetic equations, Schroedinger equations
electronic structure models, multiscale modeling and simulations, rare events simulation, computational physics, kinetic equations, nonlinear Schroedinger equations, quantum chemistry, computational statistical mechanics
rare events simulation, computational statistical mechanics, stochastic numerical methods
Signals, Images & Data
time-frequency analysis, fast algorithms, optimization, applied harmonic analysis
Jianfeng Lu is an applied mathematician interested in mathematical analysis and algorithm development for problems from computational physics, theoretical chemistry, materials science and other related fields.
More specifically, his current research focuses include:
Electronic structure and many body problems; quantum molecular dynamics; multiscale modeling and analysis; rare events and sampling techniques.
NRT-HDR: Harnessing AI for Autonomous Material Design awarded by National Science Foundation (Participants). 2020 to 2025
Innovation of Numerical Methods for High-Dimensional Problems awarded by National Science Foundation (Principal Investigator). 2020 to 2023
HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation (Co-Principal Investigator). 2019 to 2022
FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis awarded by National Science Foundation (Co-Principal Investigator). 2019 to 2022
EAGER-QAC-QSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets awarded by National Science Foundation (Principal Investigator). 2020 to 2022
Quantum Computing in Chemical and Material Sciences awarded by Department of Energy (Co-Principal Investigator). 2018 to 2021
Collaborative Research: SI2-SSI: ELSI-Infrastructure for Scalable Electronic Structure Theory awarded by National Science Foundation (Co-Principal Investigator). 2015 to 2021
CAREER: Research and training in advanced computational methods for quantum and statistical mechanics awarded by National Science Foundation (Principal Investigator). 2015 to 2020
Mathematical Problems for Electronic Structure Models awarded by National Science Foundation (Principal Investigator). 2013 to 2016
Yu, V. W. Z., et al. “ELSI — An open infrastructure for electronic structure solvers.” Computer Physics Communications, vol. 256, Nov. 2020. Scopus, doi:10.1016/j.cpc.2020.107459. Full Text
Li, W., et al. “Fisher information regularization schemes for Wasserstein gradient flows.” Journal of Computational Physics, vol. 416, Sept. 2020. Scopus, doi:10.1016/j.jcp.2020.109449. Full Text
Gao, Y., et al. “Analysis of a continuum theory for broken bond crystal surface models with evaporation and deposition effects.” Nonlinearity, vol. 33, no. 8, Aug. 2020, pp. 3816–45. Scopus, doi:10.1088/1361-6544/ab853d. Full Text
Ge, R., et al. “Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds.” Proceedings of the Annual Acm Symposium on Theory of Computing, June 2020, pp. 579–86. Scopus, doi:10.1145/3357713.3384289. Full Text
Nishimura, A., et al. “Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods.” Biometrika, vol. 107, no. 2, June 2020, pp. 365–80. Scopus, doi:10.1093/biomet/asz083. Full Text
Li, Y., et al. “Variational training of neural network approximations of solution maps for physical models.” Journal of Computational Physics, vol. 409, May 2020. Scopus, doi:10.1016/j.jcp.2020.109338. Full Text
Lu, J., and Z. Wang. “The full configuration interaction quantum monte carlo method through the lens of inexact power iteration.” Siam Journal on Scientific Computing, vol. 42, no. 1, Jan. 2020, pp. B1–29. Scopus, doi:10.1137/18M1166626. Full Text Open Access Copy
Chen, Ke, et al. “Randomized Sampling for Basis Function Construction in Generalized Finite Element Methods.” Multiscale Modeling & Simulation, vol. 18, no. 2, Society for Industrial & Applied Mathematics (SIAM), Jan. 2020, pp. 1153–77. Crossref, doi:10.1137/18m1166432. Full Text Open Access Copy
Lu, J., and S. Steinerberger. “Optimal Trapping for Brownian Motion: a Nonlinear Analogue of the Torsion Function.” Potential Analysis, Jan. 2020. Scopus, doi:10.1007/s11118-020-09845-5. Full Text
Yu, Victor, et al. “Large-scale benchmark of electronic structure solvers with the ELSI infrastructure.” Abstracts of Papers of the American Chemical Society, vol. 257, AMER CHEMICAL SOC, 2019.
Yu, Victor, et al. “ELSI: A unified software interface for Kohn-Sham electronic structure solvers.” Abstracts of Papers of the American Chemical Society, vol. 255, AMER CHEMICAL SOC, 2018.
Xian, Yin, et al. “CLASSIFICATION OF WHALE VOCALIZATIONS USING THE WEYL TRANSFORM.” 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), 2015, pp. 773–77.
The 2017 IMA Prize in Mathematics and its Applications has been awarded to Jianfeng Lu, an associate professor in the Department of Mathematics at Duke University, with secondary appointments in the Departments of Chemistry and Physics. Lu received... read more »
New faculty member applies math to solve some of the hardest questions in science Durham, NC - As a mathematician, Jianfeng Lu appreciates the abstract beauty of theories and proofs. But he also sees his craft as a powerful, pragmatic tool for... read more »