- 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.
Li, Yingzhou, and Jianfeng Lu. Optimal Orbital Selection for Full Configuration Interaction (OptOrbFCI): Pursuing Basis Set Limit under Budget.
García-Cervera, C. J., et al. “Linear-scaling subspace-iteration algorithm with optimally localized nonorthogonal wave functions for Kohn-Sham density functional theory.” Physical Review B, vol. 79, no. 11, American Physical Society (APS). Crossref, doi:10.1103/physrevb.79.115110. Full Text
Sachs, Matthias, et al. Posterior computation with the Gibbs zig-zag sampler.
Li, Lei, et al. “A stochastic version of Stein variational gradient descent for efficient sampling.” Communications in Applied Mathematics and Computational Science, vol. 15, no. 1, Mathematical Sciences Publishers, pp. 37–63. Crossref, doi:10.2140/camcos.2020.15.37. Full Text Open Access Copy
Lu, Yulong, and Jianfeng Lu. A Universal Approximation Theorem of Deep Neural Networks for Expressing Distributions.
Holst, Michael, et al. Symmetry Breaking in Density Functional Theory due to Dirac Exchange for a Hydrogen Molecule.