Gregory J. Wagner

About

Gregory J. Wagner is a Professor of Mechanical Engineering at Northwestern University and serves as the Director of Graduate Studies for Mechanical Engineering. He holds a Ph.D. and M.S. in Mechanical Engineering from Northwestern University and a B.S. in Mechanical Engineering from Boston University. His research interests focus on the development and application of computational simulation methods for multi-scale and multi-physics problems in engineering, particularly in fluid dynamics, heat transfer, and material transport. His work involves creating models for complex systems where multiple physical phenomena are coupled within a single domain or across interfaces, and solving these models using high-performance computing tools. His research includes applications such as melting and solidification in advanced manufacturing, fluid-structure interaction in biological systems, heat transfer in multi-phase flows, and multi-scale environmental transport.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Physics
• Mechanical engineering
• Algorithm
• Materials science
• Engineering
• Mechanics
• Mathematics

Selected publications

• Mesoscopic and multiscale modelling in materials

  Nature Materials · 2021 · 370 citations

  DOI

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

  Computational Materials Science · 2021 · 83 citations

  DOI

• A conservative level set method on unstructured meshes for modeling multiphase thermo-fluid flow in additive manufacturing processes

  Computer Methods in Applied Mechanics and Engineering · 2020 · 57 citations

  Senior authorCorresponding
  DOI

• Data-driven analysis of process, structure, and properties of additively manufactured Inconel 718 thin walls

  npj Computational Materials · 2022 · 48 citations

  Senior authorCorresponding

  Abstract In additive manufacturing of metal parts, the ability to accurately predict the extremely variable temperature field in detail, and relate it quantitatively to structure and properties, is a key step in predicting part performance and optimizing process design. In this work, a finite element simulation of the directed energy deposition (DED) process is used to predict the space- and time-dependent temperature field during the multi-layer build process for Inconel 718 walls. The thermal…

  PublisherOA PDFDOI

• Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond

  Computational Mechanics · 2023-05-16 · 38 citations

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Wing Kam Liu

  46 shared

• Timothy J. Carrig

  Lockheed Martin (United States)

  31 shared

• Stephen Lin

  17 shared

• Glenn T. Bennett

  16 shared

• Andrew Malm

  14 shared

• W. J. Alford

  Atrium Medical (Australia)

  13 shared

• Wentao Yan

  13 shared

• Berton E. Callicoatt

  Lockheed Martin (United States)

  12 shared

Awards & honors

• Gregory Wagner Named Bette and Neison Harris Chair in Teachi…
• Four Professors Included in Special Nature Materials Themati…

Similar researchers at Northwestern University

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• Joseph Hupph-171
• James D Thomash-160