Faculty Directory
Wing K. Liu

Walter P. Murphy Professor of Mechanical Engineering & Civil and Environmental Engineering and (by courtesy) Materials Science and Engineering


2145 Sheridan Road
Tech A326
Evanston, IL 60208-3109

847-491-7094Email Wing Liu


Mechanical Engineering


Theoretical and Applied Mechanics Graduate Program

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Ph.D. California Institute of Technology, Pasadena, CA

M.S. California Institute of Technology, Pasadena, CA

B.S. Engineering Science (Hons)
University of Illinois at Chicago, Chicago, IL

Research Interests

Mathematical scientific principles provide the fundamental understanding and allow predictions which drive new discoveries and enable future technologies. Unfortunately, development of new scientific principles is often trailing the pace of new inventions with the sheer volume of data that are being generated across multiple spatial, temporal, and parameter scales. In this context, mechanistic data science [Mechanistic Data Science for STEM Education and Applications", Liu, Gan, Fleming, to appear, Springer, December, 2021] provides the critically needed ability to combine known scientific principles with newly collected data, which will be a boon for new inventions.  

HiDeNN-AI (Liu, Wing Kam et al., “HiDeNN: An AI Platform for Scientific and Materials Systems Innovation,” Disc-ID-21-04-06-001, April, 6, 2021) is built on ten integrated modules to form a mechanistic artificial intelligence software framework with new methods and algorithms for extremely fast design, optimization, decision making, and discovery and extraction of mechanistic features simply from signals and images deployable for scientific and engineering processes. The proposed HiDeNN-AI software platform development can also be useful for advanced and additive manufacturing process design including signal analysis, data generation, for the discovery of key processing parameters for close-loop control. Using mechanistic active knowledge transfer module of HiDeNN-AI, we might be able to remove the manufacturing machines dependency and the usage of new material powders with only a limited set of experimental calibration of the new/alternative manufacturing system, and/or material powders.

Significant Recognition

  • International Association for Computational Mechanics: Gauss-Newton Medal, the highest award given by the Society (2012)
  • American Society of Mechanical Engineers (ASME): Robert Henry Thurston Lecture Award, 2007
  • U. S. Association for Computational Mechanics: John von Neumann Medal, the highest award given by USACM, 2007
  • Japan Society of Mechanical Engineers: Computational Mechanics Award, 2004
  • Cited by Institute for Scientific Information (ISI) as one of the most highly cited, influential researchers in Engineering, and an original member, highly cited researchers database (2001)
  • ASME Gustus L. Larson Memorial Award (1995)
  • Thomas J. Jaeger Prize, Int. Association for Structural Mechanics in Reactor Technology (1989)
  • ASME Pi Tau Sigma Gold Medal (1985)
  • Ralph R. Teetor Educational Award, American Society of Automotive Engineers (1983)
  • ASME Melville Medal (1979)

Significant Professional Service

  • Editor, Computational Mechanics
  • Honorary editor-in-chief of the International Journal of Computational Methods
  • Honorary editor-in-chief of the International Journal of Computational Methods
  • 2010- Present Vice President of the International Association for Computational Mechanics (Elected)
  • 2007- Founding Chair of the ASME Wide Nanotechnology Council
  • 2005 Chair, executive committee of Applied Mechanics Division of ASME (Member 2001-2006)
  • 2005 Chair, executive committee of Applied Mechanics Division of ASME (Member 2001-2006)
  • Past President of U. S. Association for Computational Mechanics
  • 2005 Chair, executive committee of Applied Mechanics Division of ASME
  • 2009- present, Visiting Distinguished World Class University Professor of Sung Kyun Kwan University, Korea

Selected Publications

  • Liu, Daoping; Yang, Hang; Elkhodary, K. I.; Tang, Shan; Liu, Wing Kam; Guo, Xu, Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks, Computer Methods in Applied Mechanics and Engineering (2022).
  • Mozaffar, Mojtaba; Liao, Shuheng; Xie, Xiaoyu; Saha, Sourav; Park, Chanwook; Cao, Jian; Liu, Wing Kam; Gan, Zhengtao, Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes, Journal of Materials Processing Technology (2022).
  • Zhang, Lei; Lu, Ye; Tang, Shaoqiang; Liu, Wing Kam, HiDeNN-TD, Computer Methods in Applied Mechanics and Engineering (2022).
  • Kats, Dmitriy; Wang, Zhidong; Gan, Zhengtao; Liu, Wing Kam; Wagner, Gregory J; Lian, Yanping, A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition, Computational Materials Science (2022).
  • Tajdari, M.; Maqsood, A.; Li, H.; Saha, S.; Sarwark, J. F.; Liu, W. K., Artificial intelligence data-driven 3D model for AIS, IOS Press BV:141-145 (2021).
  • Xie, Xiaoyu; Bennett, Jennifer; Saha, Sourav; Lu, Ye; Cao, Jian; Liu, Wing Kam; Gan, Zhengtao, Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing, npj Computational Materials (2021).
  • Gan, Zhengtao; Kafka, Orion L.; Parab, Niranjan; Zhao, Cang; Fang, Lichao; Heinonen, Olle; Sun, Tao; Liu, Wing Kam, Universal scaling laws of keyhole stability and porosity in 3D printing of metals, Nature communications (2021).
  • Lian, Yan Ping; Dallmann, Jonathan; Sonin, Benjamin; Roche, Kevin R.; Packman, Aaron I.; Liu, Wing Kam; Wagner, Gregory J., Double Averaging Analysis Applied to a Large Eddy Simulation of Coupled Turbulent Overlying and Porewater Flow, Water Resources Research (2021).

In the Classroom

Finite Element Methods in Mechanics

To learn the basic theory behind the finite element method (FEM); how to program the finite element method using MATLAB as a programming tool; to learn a general commercial FEM code to write interface programs and solve typical engineering problems.

Finite Elements for Design and Optimization

The scope of the course is to provide the analytical and computational tools necessary for the design of complex structural and material systems for modern engineering applications, ranging from structural engineering to micro and nanotechnology.

Advanced Finite Element Methods I

Gain theoretical, programming, and application knowledge of nonlinear finite element methods. Understand the associated continuum mechanics and its finite element implementation and applications.

Advanced Finite Element Methods II

Arbitrary Lagrangian Eulerian (ALE) formulations, Non-linear materials, and introduction to computational fracture mechanics; using ABAQUS, programming VUMAT, UMAT.

Multi-scale Modeling and Simulation in Solid Mechanics

Understand the underlying principles of molecular dynamics. Gain proficiency in designing molecular dynamics simulations using available software (LAMMPS). Understand the connection between information available on small (atomistic) and large (continuum) scales. Applications: Nanostructure materials: Nanowires: single crystal Si.  Nano Carbons: nanotube; Polymer nano-composite: polymer mechanics, polymer and polymer-fillers modeling; Multi-scale modeling.