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:

Office Location:	242 Physics Bldg, 120 Science Drive, Durham, NC 27708
Office Phone:	(919) 660-2875
Email Address:
Web Page:	http://www.math.duke.edu/~jianfeng/

Teaching (Fall 2023):

• MATH 555.01, ORDINARY DIFF EQUATIONS Synopsis

  Gross Hall 324, TuTh 10:05 AM-11:20 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  

• Kyle Thicke  
• Chao Xu  

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)

1. Wang, M; Lu, J, Neural Network-Based Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions, Communications in Mathematics and Statistics, vol. 11 no. 1 (March, 2023), pp. 21-57 [doi]  [abs]
2. 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. 790-798 [doi]  [abs]
3. 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. 1141-1209, American Mathematical Society (AMS) [doi]  [abs]
4. Chen, Z; Lu, J; Lu, Y; Zhou, S, A REGULARITY THEORY FOR STATIC SCHRÖDINGER EQUATIONS ON R ^d IN SPECTRAL BARRON SPACES, Siam Journal on Mathematical Analysis, vol. 55 no. 1 (January, 2023), pp. 557-570 [doi]  [abs]
5. 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]

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.      
• FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis, National Science Foundation, 2019/10-2023/09.      
• 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.      
• 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.