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 (Spring 2023):
- MATH 660.01, NUMERICAL PARTIAL DIFF EQNS
- Gross Hall 355, TuTh 10:15 AM-11:30 AM
- Office Hours:
- By email appointments
- Research Interests:
Mathematical analysis and algorithm development for problems from
computational physics, theoretical chemistry, materials science and others.
Electronic structure and many body problems;
Multiscale modeling and analysis; and
Rare events and sampling techniques.
- Areas of Interest:
- Applied Mathematics
Partial Differential Equations
- 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
- 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
pp. 790-798 [doi] [abs]
- Wang, M; Lu, J, Neural Network-Based Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions,
Communications in Mathematics and Statistics
(January, 2023) [doi] [abs]
- 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
(December, 2022) [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
pp. 1141-1209, American Mathematical Society (AMS) [doi] [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
pp. 631-662 [doi] [abs]
- Recent Grant Support
- RTG: Training Tomorrow's Workforce in Analysis and Applications, National Science Foundation, 2021/07-2026/06.
- NRT-HDR: Harnessing AI for Autonomous Material Design, National Science Foundation, 2020/09-2025/08.
- HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/10-2023/09.
- EAGER-QAC-QSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets, National Science Foundation, 2020/09-2023/08.
- Innovation of Numerical Methods for High-Dimensional Problems, National Science Foundation, 2020/07-2023/06.
- FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis, National Science Foundation, 2019/10-2022/09.
- Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/09-2022/09.
- Collaborative Research: SI2-SSI: ELSI-Infrastructure for Scalable Electronic Structure Theory, National Science Foundation, 1450280, 2015/06-2022/05.
- Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/09-2021/09.
- CAREER: Research and training in advanced computational methods for quantum and statistical mechanics, National Science Foundation, DMS-1454939, 2015/09-2020/08.