World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
36
Citations
4646
World Ranking
2556
National Ranking
911

WaiChing Sun 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 WaiChing Sun 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: 119 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

WaiChing Sun 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 WaiChing Sun 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: 36 D-Index — 28th percentile

28% of scientists in this discipline score the same or lower.

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

Overview

WaiChing Sun is affiliated with Columbia University in the United States. Their research work primarily falls within the field of Engineering, with a focus on several subfields such as Mechanics of Materials, Computational Mechanics, Statistical and Nonlinear Physics, Materials Chemistry, and Atmospheric Science.

The scientist's publication record includes a significant number of papers published in a variety of scholarly venues. Frequent publication venues include:

  • Computer Methods in Applied Mechanics and Engineering
  • arXiv (Cornell University)
  • International Journal for Numerical and Analytical Methods in Geomechanics
  • International Journal for Numerical Methods in Engineering
  • Archives of Computational Methods in Engineering

WaiChing Sun's research covers a range of topics that intersect Engineering and Materials Science, with key themes including:

  • Model Reduction and Neural Networks
  • Numerical methods in engineering
  • Elasticity and Material Modeling
  • Composite Material Mechanics
  • Probabilistic and Robust Engineering Design
  • Rock Mechanics and Modeling
  • Machine Learning in Materials Science

Several papers highlight key contributions by WaiChing Sun and collaborators, including:

  • Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity (2020), published in Computer Methods in Applied Mechanics and Engineering
  • Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (2021), published in Computer Methods in Applied Mechanics and Engineering
  • SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020), published in Computer Methods in Applied Mechanics and Engineering
  • A Review on Data-Driven Constitutive Laws for Solids (2024), published in Archives of Computational Methods in Engineering
  • Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning (2022), published in Scientific Reports

WaiChing Sun collaborates frequently with a number of researchers in their field. Frequent coauthors include Nikolaos N. Vlassis, Ran Ma, Bahador Bahmani, Hyoung Suk Suh, and Mian Xiao. These collaborative efforts contribute to a diverse and interdisciplinary approach within their areas of research.

Best Publications

  • A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

    Kun Wang;WaiChing Sun

  • Coupled phase-field and plasticity modeling of geological materials: From brittle fracture to ductile flow

    Jinhyun Choo;Jinhyun Choo;Wai Ching Sun

  • Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity

    Nikolaos N. Vlassis;Ran Ma;WaiChing Sun

  • A mixed-mode phase field fracture model in anisotropic rocks with consistent kinematics

    Eric C. Bryant;WaiChing Sun

  • Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening

    Nikolaos N. Vlassis;WaiChing Sun

  • Stress-induced anisotropy in granular materials: fabric, stiffness, and permeability

    Matthew R. Kuhn;WaiChing Sun;Qi Wang

  • A stabilized assumed deformation gradient finite element formulation for strongly coupled poromechanical simulations at finite strain

    WaiChing Sun;Jakob T. Ostien;Andrew G. Salinger

  • Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning

    Kun Wang;WaiChing Sun

  • Connecting microstructural attributes and permeability from 3D tomographic images of in situ shear-enhanced compaction bands using multiscale computations

    WaiChing Sun;José E. Andrade;John W. Rudnicki;Peter Eichhubl

  • SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

    Yousef Heider;Yousef Heider;Kun Wang;WaiChing Sun

  • Computational thermo-hydro-mechanics for multiphase freezing and thawing porous media in the finite deformation range

    SeonHong Na;WaiChing Sun

  • A nonlocal multiscale discrete‐continuum model for predicting mechanical behavior of granular materials

    Yang Liu;WaiChing Sun;Zifeng Yuan;Jacob Fish

  • Multiscale method for characterization of porous microstructures and their impact on macroscopic effective permeability

    WaiChing Sun;Jose E. Andrade;John W. Rudnicki

  • Cracking and damage from crystallization in pores: Coupled chemo-hydro-mechanics and phase-field modeling

    Jinhyun Choo;Jinhyun Choo;WaiChing Sun

  • A multiscale DEM-LBM analysis on permeability evolutions inside a dilatant shear band

    Wai Ching Sun;Matthew R. Kuhn;John W. Rudnicki

  • A semi-implicit discrete-continuum coupling method for porous media based on the effective stress principle at finite strain

    Kun Wang;WaiChing Sun

  • A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation

    Kun Wang;WaiChing Sun;Qiang Du

  • A stabilized finite element formulation for monolithic thermo‐hydro‐mechanical simulations at finite strain

    WaiChing Sun

  • A phase field framework for capillary-induced fracture in unsaturated porous media: Drying-induced vs. hydraulic cracking

    Yousef Heider;Yousef Heider;WaiChing Sun

  • Effects of spatial heterogeneity and material anisotropy on the fracture pattern and macroscopic effective toughness of Mancos Shale in Brazilian tests

    SeonHong Na;WaiChing Sun;Mathew D. Ingraham;Hongkyu Yoon

  • ALBANY: USING COMPONENT-BASED DESIGN TO DEVELOP A FLEXIBLE, GENERIC MULTIPHYSICS ANALYSIS CODE

    Andrew G. Salinger;Roscoe A. Bartlett;Andrew M. Bradley;Qiushi Chen

  • Modeling the hydro-mechanical responses of strip and circular punch loadings on water-saturated collapsible geomaterials

    WaiChing Sun;Qiushi Chen;Jakob T. Ostien

  • Coupled flow network and discrete element modeling of injection-induced crack propagation and coalescence in brittle rock

    Guang Liu;Guang Liu;WaiChing Sun;Steven M. Lowinger;ZhenHua Zhang

  • Computational thermomechanics of crystalline rock, Part I: A combined multi-phase-field/crystal plasticity approach for single crystal simulations

    SeonHong Na;WaiChing Sun

  • An updated Lagrangian LBM–DEM–FEM coupling model for dual-permeability fissured porous media with embedded discontinuities

    Kun Wang;WaiChing Sun

Frequent Co-Authors

John W. Rudnicki
John W. Rudnicki Northwestern University
Jacob Fish
Jacob Fish Columbia University
Andrew G. Salinger
Andrew G. Salinger Sandia National Laboratories
Qiang Du
Qiang Du Columbia University
Ronaldo I. Borja
Ronaldo I. Borja Stanford University
Jidong Zhao
Jidong Zhao Hong Kong University of Science and Technology
Bernd Markert
Bernd Markert RWTH Aachen University
Brad L. Boyce
Brad L. Boyce Sandia National Laboratories
Leon Mishnaevsky
Leon Mishnaevsky Technical University of Denmark

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