Jianfeng Lu, Professor
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.  Contact Info:
Teaching (Fall 2023):
 MATH 555.01, ORDINARY DIFF EQUATIONS
Synopsis
 Gross Hall 324, TuTh 10:05 AM11:20 AM
 Office Hours:
 By email appointments
 Education:
Ph.D.  Princeton University  2009 
BS  Peking University  2005 
 Specialties:

Applied Math
 Research Interests:
Mathematical analysis and algorithm development for problems from
computational physics, theoretical chemistry, materials science and others.
More specifically:
Electronic structure and many body problems;
Multiscale modeling and analysis; and
Rare events and sampling techniques.
 Areas of Interest:
Applied Mathematics Partial Differential Equations Probability Numerical Analysis Scientific Computing
 Curriculum Vitae
 Current Ph.D. Students
 Postdocs Mentored
 Haizhao Yang (July, 2015  present)
 Zhennan Zhou (August, 2014  present)
 Undergraduate Research Supervised
 Jeremy Tay (September, 2015  December, 2015)
 Fuchsia Chen (January, 2015  September, 2015)
 Leslie Lei (May, 2013  May, 2014)
 Recent Publications
(More Publications)
 Cao, Y; Lu, J; Wang, L, On Explicit L^{2} Convergence Rate Estimate for Underdamped Langevin Dynamics,
Archive for Rational Mechanics and Analysis, vol. 247 no. 5
(October, 2023) [doi] [abs]
 Wang, M; Lu, J, Neural NetworkBased Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions,
Communications in Mathematics and Statistics, vol. 11 no. 1
(March, 2023),
pp. 2157 [doi] [abs]
 Bierman, J; Li, Y; Lu, J, Improving the Accuracy of Variational Quantum Eigensolvers with Fewer Qubits Using Orbital Optimization.,
Journal of Chemical Theory and Computation, vol. 19 no. 3
(February, 2023),
pp. 790798 [doi] [abs]
 Cai, Z; Lu, J; Yang, S, NUMERICAL ANALYSIS FOR INCHWORM MONTE CARLO METHOD: SIGN PROBLEM AND ERROR GROWTH,
Mathematics of Computation, vol. 92 no. 341
(January, 2023),
pp. 11411209, American Mathematical Society (AMS) [doi] [abs]
 Chen, Z; Lu, J; Lu, Y; Zhou, S, A REGULARITY THEORY FOR STATIC SCHRÃ–DINGER EQUATIONS ON R ^{d} IN SPECTRAL BARRON SPACES,
Siam Journal on Mathematical Analysis, vol. 55 no. 1
(January, 2023),
pp. 557570 [doi] [abs]
 Recent Grant Support
 Innovation of Numerical Methods for HighDimensional Partial Differential Equations, National Science Foundation, 2023/082026/07.
 New computational methods to dynamically pinpointing the subregions carrying diseaseassociated rare variants, National Institutes of Health, 2022/092026/07.
 RTG: Training Tomorrow's Workforce in Analysis and Applications, National Science Foundation, 2021/072026/06.
 NRTHDR: Harnessing AI for Autonomous Material Design, National Science Foundation, 2020/092025/08.
 FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis, National Science Foundation, 2019/102023/09.
 HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/102023/09.
 EAGERQACQSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets, National Science Foundation, 2020/092023/08.
 Innovation of Numerical Methods for HighDimensional Problems, National Science Foundation, 2020/072023/06.
 Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/092022/09.
 Collaborative Research: SI2SSI: ELSIInfrastructure for Scalable Electronic Structure Theory, National Science Foundation, 1450280, 2015/062022/05.
 Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/092021/09.
