- 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.
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
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
Zhu, Wei, et al. “Stop Memorizing: A Data-Dependent Regularization Framework for Intrinsic Pattern Learning.” Siam J. Math. Data Sci., vol. 1, 2019, pp. 476–96.
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.