Wing Liu

About

Wing Kam Liu is the Walter P. Murphy Professor of Mechanical Engineering and Civil and Environmental Engineering at Northwestern University, with courtesy appointments in Materials Science and Engineering. His research focuses on the development and application of mathematical scientific principles to understand and predict phenomena in engineering and materials science. Liu emphasizes mechanistic data science, which combines known scientific principles with newly collected data to facilitate new inventions and technological advancements. He has developed innovative AI platforms such as HiDeNN-AI, a mechanistic artificial intelligence software framework designed for rapid design, optimization, decision making, and discovery in scientific and engineering processes, including advanced manufacturing and material processing. Liu's contributions have been recognized through numerous prestigious awards, including the Gauss-Newton Medal from the International Association for Computational Mechanics, the Robert Henry Thurston Lecture Award from ASME, and the John von Neumann Medal from USACM. He has served in significant professional roles, such as Vice President of the International Association for Computational Mechanics and founding chair of the ASME Wide Nanotechnology Council. His work has had a substantial impact on computational mechanics, materials modeling, and additive manufacturing, making him a highly cited and influential researcher in his field.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Machine Learning
• Materials science
• Mechanical engineering
• Physics
• Political Science
• Applied mathematics

Selected publications

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

  Archives of Computational Methods in Engineering · 2022 · 275 citations

  1st authorCorresponding

  Abstract This document presents comprehensive historical accounts on the developments of finite element methods (FEM) since 1941, with a specific emphasis on developments related to solid mechanics. We present a historical overview beginning with the theoretical formulations and origins of the FEM, while discussing important developments that have enabled the FEM to become the numerical method of choice for so many problems rooted in solid mechanics.

  DOI

• Universal scaling laws of keyhole stability and porosity in 3D printing of metals

  Nature Communications · 2021 · 226 citations

  Senior authorCorresponding

  Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materi…

  DOI

• Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering

  Computer Methods in Applied Mechanics and Engineering · 2020 · 180 citations

  Senior authorCorresponding
  DOI

• Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

  npj Computational Materials · 2021 · 134 citations

  Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperat…

  PublisherOA PDFDOI

• A physics-informed machine learning method for predicting grain structure characteristics in directed energy deposition

  Computational Materials Science · 2021 · 83 citations

  DOI

Recent grants

• Computational Multiresolution Mechanics of Solids and Structures

  NSF · $150k · 2008–2011

• Manipulating Nanoparticle-Modified Melt Pool Dynamics in Additive Manufacturing

  NSF · $874k · 2019–2024

• Data-driven Multiscale Damage and Failure Prediction

  NSF · $569k · 2018–2022

Frequent coauthors

• Ted Belytschko

  61 shared

• Gregory J. Wagner

  Northwestern University

  46 shared

• Shan Tang

  China Jiliang University

  35 shared

• Shaofan Li

  University of California, Berkeley

  34 shared

• Ying Li

  33 shared

• Zhengtao Gan

  The University of Texas at El Paso

  32 shared

• Jian Cao

  31 shared

• Dong Qian

  The University of Texas at Dallas

  30 shared

Awards & honors

• Gauss-Newton Medal, the highest award given by the Internati…
• Robert Henry Thurston Lecture Award, American Society of Mec…
• John von Neumann Medal, the highest award given by USACM (20…
• Computational Mechanics Award, Japan Society of Mechanical E…
• ASME Gustus L. Larson Memorial Award (1995)