- Associate 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.
Lu, Jianfeng, and Stefan Steinerberger. On Pointwise Products of Elliptic Eigenfunctions.
Lu, Yulong, et al. Accelerating Langevin Sampling with Birth-death.
Lu, Jianfeng, et al. Deep Network Approximation for Smooth Functions.
Nishimura, Akihiko, et al. Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods.
Thicke, Kyle, et al. Computing edge states without hard truncation.
Khoo, Yuehaw, et al. Solving parametric PDE problems with artificial neural networks. Open Access Copy
Nolen, J. H., et al. “Scaling limit of the Stein variational gradient descent: the mean field regime.” Siam Journal on Mathematical Analysis, Society for Industrial and Applied Mathematics.