Shaofan Li

Research Interests
AI & ML applications in engineering, Structural & Computational mechanics, Finite element methods, Nonlinear and nonlocal continuum mechanics, Multiscale simulation of materials, Modeling, and simulation of material failures, and Modeling/simulation of environmental fluid dynamics, Nanoscale characterization of cementitious materials.

783 Davis Hall

Office Hours

Tuesdays and Thursdays, 4:00 pm to 6:00 pm

Li headshot

Shaofan Li is a Professor of Civil and Environmental Engineering at UC Berkeley. Li's current research focuses on (1) Artificial-intelligence-aided design methodology and machine learning-based computational modeling methods, such as AI-based additive manufacturing modeling, AI-based inverse finite element method, traffic accident forensic analysis, and wire fire occurrence prediction. (2) Computational failure mechanics e.g. nonlocal peridynamics modeling and phase-field modeling of fracture. (3) Computational environmental fluid mechanics, such as prediction of human cough spreading and nonlocal computational fluid mechanics modeling, and (4) Nano- and mesoscale modeling and experimentation of sustainable cementitious materials.  He also received the prestigious International Association of Computational Mechanics Fellows Award in 2017 and the National Science Foundation CAREER award in 2003, among other notable accolades.


Ph.D., Mechanical Engineering, Northwestern University, 1997

M.S., Aerospace Engineering, University of Florida, 1993

M.S., Computational Mechanics, Huazhong University of Science and Technology, 1989

B.S., Mechanical Engineering, East China University of Science and Technology, 1982

Li’s research focuses on developing physics-based phase-field modeling and simulation of fractures, computational methods for modeling and simulation of transmission and parthenogenesis process of infectious diseases, AI-based methods or statistic machine learning methods for 3D printing thermal compensation and inverse forensic analysis of car collision, and other engineering applications. His research group is dedicated to research conducted in the field of computational mechanics, focusing on soft matter mechanics, atomistic and multiscale simulation, computational nano-mechanics, and computational fracture mechanics. Here are a few of the research projects Li is currently working on below:

  • Developing Machine-learning based Multiscale crystal defect dynamics (MCDD): Li’s research group has proposed a novel concept of geometrically compatible dislocation patterns and applied it to study nanoscale plasticity. They have been developing an atomistic-informed dislocation pattern theory and its computational formulations to model crystal plasticity.
  • Con-current multiscale molecular dynamics: Li’s research group has been developing a con-current multiscale molecular dynamics to simulate the phase transformation of crystalline solids at the nanoscale. His research findings extended this theory to amorphous materials and use it to model and simulate plasticity in amorphous materials.
  • Nonlocal fluid dynamics and Peridynamics: Li’s research group has applied mesh-free particle methods and peridynamics methods to simulate material and structure failure at multiscale. We have used mesh-free methods to simulate large-scale ductile failure in steel structures, and they have applied the peridynamics method to simulate soil fragmentation and fracture in metals, ceramics, concrete structures, and in ice sheets. As part of this research, they have developed a nonlocal fluid dynamic --- Updated Lagrangian Particle Hydrodynamics (ULPH) to model and simulate fluid flows from cough-jets to multiphase flow to fluid-structure interaction problems.


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