World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
60
Citations
15615
World Ranking
692
National Ranking
302

Shaofan Li publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Shaofan Li sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 332 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Shaofan Li D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Shaofan Li sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 60 D-Index — 80th percentile

80% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Overview

Shaofan Li is affiliated with the University of California, Berkeley in the United States and has a research focus primarily within the field of Engineering. Their scholarly output spans multiple subfields including Mechanics of Materials, Civil and Structural Engineering, Materials Chemistry, Computational Mechanics, and Mechanical Engineering.

The topics covered by their work include numerical methods in engineering, geotechnical engineering and underground structures, fluid dynamics simulations and interactions, composite material mechanics, concrete and cement materials research, rock mechanics and modeling, and structural health monitoring techniques.

Their recent papers include:

  • Eighty Years of the Finite Element Method: Birth, Evolution, and Future (2022) published in Archives of Computational Methods in Engineering
  • A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives (2021) published in Archives of Computational Methods in Engineering
  • Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete (2020) published in Construction and Building Materials
  • A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials (2022) published in Archives of Computational Methods in Engineering
  • Application and modelling of Shape-Memory Alloys for structural vibration control: State-of-the-art review (2022) published in Construction and Building Materials

Frequently collaborating with other researchers, Shaofan Li has several frequent coauthors:

  • Jinyang Jiang
  • Xuan Hu
  • Yuxi Xie
  • A-Man Zhang
  • Chengyao Liang

Their publications appear regularly in the following venues:

  • Computer Methods in Applied Mechanics and Engineering
  • SSRN Electronic Journal
  • Computational Mechanics
  • arXiv (Cornell University)
  • Archives of Computational Methods in Engineering

Shaofan Li has contributed to book publications as well, with works published by Cambridge University Press and Springer Nature (Netherlands). Titles include "Introduction to Computational Nanomechanics" (2022) and "Computational and Experimental Simulations in Engineering" (2023).

Best Publications

  • Meshfree and particle methods and their applications

    Shaofan Li;Wing Kam Liu

  • Reproducing kernel particle methods for structural dynamics

    Wing Kam Liu;Sukky Jun;Shaofan Li;Jonathan Adee

  • Moving least-square reproducing kernel methods (I) Methodology and convergence

    Wing Kam Liu;Shaofan Li;Ted Belytschko

  • Swelling of Graphene Oxide Membranes in Aqueous Solution: Characterization of Interlayer Spacing and Insight into Water Transport Mechanisms

    Sunxiang Zheng;Qingsong Tu;Jeffrey J. Urban;Shaofan Li

  • Understanding the Aqueous Stability and Filtration Capability of MoS2 Membranes

    Zhongying Wang;Qingsong Tu;Sunxiang Zheng;Jeffrey J. Urban

  • INTRODUCTION TO MICROMECHANICS AND NANOMECHANICS

    Shaofan Li;Gang Wang

  • Finite difference calculus invariant structure of a class of algorithms for the nonlinear Klein-Gordon equation

    S. Li;L. Vu-Quoc

  • Eighty Years of the Finite Element Method: Birth, Evolution, and Future.

    Wing Kam Liu;Shaofan Li;Harold Park

  • Reproducing kernel hierarchical partition of unity, Part I—formulation and theory

    Shaofan Li;Wing Kam Liu

  • A Critical Review on Structural Health Monitoring: Definitions, Methods, and Perspectives

    Vahid Reza Gharehbaghi;Ehsan Noroozinejad Farsangi;Mohammad Noori;T. Y. Yang

  • Numerical simulations of large deformation of thin shell structures using meshfree methods

    S. Li;W. Hao;W. K. Liu

  • Mesh-free Galerkin simulations of dynamic shear band propagation and failure mode transition

    Shaofan Li;Wing Kam Liu;Ares J. Rosakis;Ted Belytschko

  • Reproducing kernel element method. Part I: Theoretical formulation

    Wing Kam Liu;Weimin Han;Hongsheng Lu;Shaofan Li

  • A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials

    Unknown

  • Dynamic crack propagation in piezoelectric materials—Part I. Electrode solution

    Shaofan Li;Peter A. Mataga

  • On criteria for dynamic adiabatic shear band propagation

    Sergey N. Medyanik;Wing Kam Liu;Shaofan Li

  • Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete

    Miao Su;Miao Su;Qingyu Zhong;Hui Peng;Shaofan Li

  • Moving least-square reproducing kernel method Part II: Fourier analysis

    Shaofan Li;Wing Kam Liu

  • Perfectly matched multiscale simulations

    Albert C. To;Shaofan Li

  • A Peridynamics-SPH modeling and simulation of blast fragmentation of soil under buried explosive loads

    Houfu Fan;Shaofan Li

  • Dynamic crack propagation in piezoelectric materials—Part II. Vacuum solution

    Shaofan Li;Peter A. Mataga

  • Peridynamics simulations of geomaterial fragmentation by impulse loads

    Xin Lai;Xin Lai;Bo Ren;Houfu Fan;Shaofan Li

  • A hybrid peridynamics–SPH simulation of soil fragmentation by blast loads of buried explosive

    Houfu Fan;Guy Leshem Bergel;Shaofan Li

  • Multiple scale meshfree methods for damage fracture and localization

    Wing Kam Liu;Su Hao;Ted Belytschko;Shao Fan Li

Frequent Co-Authors

Wing Kam Liu
Wing Kam Liu Northwestern University
Roger A. Sauer
Roger A. Sauer Ruhr University Bochum
Dong Qian
Dong Qian The University of Texas at Dallas
Ted Belytschko
Ted Belytschko Northwestern University
Jian Cao
Jian Cao Northwestern University
Albert C. To
Albert C. To University of Pittsburgh
A-Man Zhang
A-Man Zhang Harbin Engineering University
Xin-Lin Gao
Xin-Lin Gao Southern Methodist University
Baoxia Mi
Baoxia Mi University of California, Berkeley
Jeffrey J. Urban
Jeffrey J. Urban Lawrence Berkeley National Laboratory

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

While Mechanical and Aerospace Engineering are highly specialized fields, exploring related disciplines like communication sciences and disorders can expand your career opportunities. Many institutions offer accessible programs, such as the communication sciences and disorders programs, that focus on speech and language pathology. These programs often provide a solid foundation for those interested in complementary fields.

Cost is a major consideration when choosing an online degree. Fortunately, resources such as speech pathology degree online cost can help prospective students budget effectively by highlighting tuition and fees for various schools.

Veterans have access to a range of tailored educational options. Programs like online speech pathology degrees for veterans offer unique benefits, including financial support and flexible schedules, making it easier for them to transition into new careers.

If you are looking to fast-track your education, consider the accelerated speech pathology programs. These intensive courses allow students to complete their degrees more quickly, preparing them for the workforce in less time.

Best Scientists Citing Shaofan Li

Trending Scientists