Professor of Mathematics
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.
Appointments and Affiliations
- Professor of Mathematics
- Office Location: 242 Physics Bldg, 120 Science Drive, Durham, NC 27708
- Office Phone: (919) 660-2875
- Email Address: firstname.lastname@example.org
- Ph.D. Princeton University, 2009
Awards, Honors, and Distinctions
- IMA Prize in Mathematics and its Applications. Institute of Mathematics and its Applications. 2017
- CAREER Award. National Science Foundation. 2015
- Sloan Research Fellowship. Alfred P. Sloan Foundation. 2013
- Porter Ogden Jacobus Fellowship. Princeton University. 2008
- MATH 394: Research Independent Study
- MATH 493: Research Independent Study
- MATH 494: Research Independent Study
- MATH 631: Measure and Integration
- MATH 660: Numerical Partial Differential Equations
- MATH 690-60: Topics in Numerical Methods
- MATH 690-70: Topics in Applied Mathematics
- PHYSICS 590: Selected Topics in Theoretical Physics
In the News
- Pratt Researchers to Harness Computational Power to Solve Time-Intensive Calculations (Jul 10, 2015)
- Sloan Foundation Names Charbonneau, Lu as 2013 Research Fellows (Feb 15, 2013)
- Holst, M; Hu, H; Lu, J; Marzuola, JL; Song, D; Weare, J, Symmetry Breaking and the Generation of Spin Ordered Magnetic States in Density Functional Theory Due to Dirac Exchange for a Hydrogen Molecule, Journal of Nonlinear Science, vol 32 no. 6 (2022) [10.1007/s00332-022-09845-2] [abs].
- He, Y; Balasubramanian, K; Sriperumbudur, BK; Lu, J, Regularized Stein Variational Gradient Flow (2022) [abs].
- Craig, K; Liu, JG; Lu, J; Marzuola, JL; Wang, L, A proximal-gradient algorithm for crystal surface evolution, Numerische Mathematik, vol 152 no. 3 (2022), pp. 631-662 [10.1007/s00211-022-01320-0] [abs].
- Marwah, T; Lipton, ZC; Lu, J; Risteski, A, Neural Network Approximations of PDEs Beyond Linearity: Representational
Perspective (2022) [abs].
- Chen, Z; Liu, J; Wang, X; Lu, J; Yin, W, On Representing Mixed-Integer Linear Programs by Graph Neural Networks (2022) [abs].