Jianfeng Lu

Jianfeng Lu
  • Associate Professor of Mathematics
  • Associate Professor of Physics (Secondary)
  • Associate Professor of Chemistry (Secondary)
  • Associate Professor in Physics (Secondary)
External address: 242 Physics Bldg, 120 Science Drive, Durham, NC 27708
Internal office address: Mathematics Department, Duke University, Box 90320, Durham, NC 27708
Phone: (919) 660-2875

Research Areas and Keywords

Analysis

electronic structure models, calculus of variations, semiclassical analysis

Computational Mathematics

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

Mathematical Physics

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

Physical Modeling

electronic structure models, multiscale modeling and simulations, rare events simulation, computational physics, kinetic equations, nonlinear Schroedinger equations, quantum chemistry, computational statistical mechanics

Probability

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.

Education & Training
  • Ph.D., Princeton University 2009

Lin, L., et al. “Numerical methods for Kohn-Sham density functional theory.” Acta Numerica, vol. 28, May 2019, pp. 405–539. Scopus, doi:10.1017/S0962492919000047. Full Text

Khoo, Yuehaw, et al. “Solving for high-dimensional committor functions using artificial neural networks.” Research in the Mathematical Sciences, vol. 6, no. 1, Springer Science and Business Media LLC, Mar. 2019. Crossref, doi:10.1007/s40687-018-0160-2. Full Text

Lu, J., and E. Vanden-Eijnden. “Methodological and Computational Aspects of Parallel Tempering Methods in the Infinite Swapping Limit.” Journal of Statistical Physics, vol. 174, no. 3, Feb. 2019, pp. 715–33. Scopus, doi:10.1007/s10955-018-2210-y. Full Text Open Access Copy

Li, Yingzhou, and Jianfeng Lu. “Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods.” Transactions of Mathematics and Its Applications, vol. 3, no. 1, Oxford University Press (OUP), Feb. 2019. Crossref, doi:10.1093/imatrm/tnz001. Full Text Open Access Copy

Martinsson, A., et al. “The simulated tempering method in the infinite switch limit with adaptive weight learning.” Journal of Statistical Mechanics: Theory and Experiment, vol. 2019, no. 1, Jan. 2019. Scopus, doi:10.1088/1742-5468/aaf323. Full Text

Huang, H., et al. “Learning interacting particle systems: Diffusion parameter estimation for aggregation equations.” Mathematical Models and Methods in Applied Sciences, vol. 29, no. 1, Jan. 2019, pp. 1–29. Scopus, doi:10.1142/S0218202519500015. Full Text Open Access Copy

Lu, J., et al. “Scaling limit of the Stein variational gradient descent: The mean field regime.” Siam Journal on Mathematical Analysis, vol. 51, no. 2, Jan. 2019, pp. 648–71. Scopus, doi:10.1137/18M1187611. Full Text

Gauckler, L., et al. “Trigonometric integrators for quasilinear wave equations.” Mathematics of Computation, vol. 88, no. 316, Jan. 2019, pp. 717–49. Scopus, doi:10.1090/mcom/3339. Full Text

Yingzhou, L. I., et al. “Coordinatewise descent methods for leading eigenvalue problem.” Siam Journal on Scientific Computing, vol. 41, no. 4, Jan. 2019, pp. A2681–716. Scopus, doi:10.1137/18M1202505. Full Text

Agazzi, Andrea, and Jianfeng Lu. “Temporal-difference learning for nonlinear value function approximation in the lazy training regime.Corr, vol. abs/1905.10917, 2019.

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