Zhengtao (Jake) Gan

About

Dr. Zhengtao (Jake) Gan directs the Mesoscopic Science in Advanced Manufacturing (Meso-AM) Research Group. His research mission is to reveal nano- and meso-scale material and energy transport processes in advanced manufacturing through synchrotron radiation experiments, mesoscopic phase-field modeling, and machine learning-based digital twins.

He is the author/co-author of the book Mechanistic Data Science for STEM Education and Applications (Springer, 2021) and maintains an active educational presence through tutorials on JAX programming, finite element methods, and computational fluid dynamics.

PHOENIX — a hybrid physics-based and AI-accelerated Fortran + OpenMP solver for simulating process-to-microstructure evolution in laser-based additive manufacturing (LPBF, DED).

ASU Technology Center, Mesa, AZ

Research

Awards & Honors

Selected Publications

Featured: Data-driven discovery of dimensionless numbers and governing laws (Nature Communications, 2022)

1. 1.
   Universal scaling laws of keyhole stability and porosity in 3D printing of metals

   Gan, Z., Kafka, O.L., Parab, N., Zhao, C., Fang, L., Heinonen, O., Sun, T. and Liu, W.K.

   Nature Communications, 12(1), pp.1–8 (2021)
2. 2.
   Data-driven discovery of dimensionless numbers and governing laws from scarce measurements

   Xie, X., Samaei, A., Guo, J., Liu, W.K. and Gan, Z.

   Nature Communications, 13(1), pp.1–11 (2022)
3. 3.
   Physics-embedded graph network for accelerating phase-field simulation

   Xue, T., Gan, Z., Liao, S. and Cao, J.

   npj Computational Materials, 8, 201 (2022)
4. 4.
   Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing

   Xie, X., Bennett, J., Saha, S., Lu, Y., Cao, J., Liu, W.K. and Gan, Z.

   npj Computational Materials, 7(1), pp.1–12 (2021)
5. 5.
   Mechanistic artificial intelligence for modeling, design, and control of advanced manufacturing

   Mozaffar, M., Liao, S., Xie, X., Saha, S., Park, C., Cao, J., Liu, W.K. and Gan, Z.

   Journal of Materials Processing Technology, p.117485 (2021)
6. 6.
   Benchmark study of thermal behavior, surface topography, and dendritic microstructure in selective laser melting

   Gan, Z., Lian, Y., Lin, S.E., Jones, K.K., Liu, W.K. and Wagner, G.J.

   Integrating Materials and Manufacturing Innovation, 8(2), pp.178–193 (2019)
7. 7.
   A cellular automaton finite volume method for microstructure evolution during additive manufacturing

   Lian, Y., Gan, Z., Yu, C., Kats, D., Liu, W.K. and Wagner, G.J.

   Materials & Design, 169, p.107672 (2019)
8. 8.
   Modeling of thermal behavior and mass transport in multi-layer laser additive manufacturing

   Gan, Z., Liu, H., Li, S., He, X. and Yu, G.

   International Journal of Heat and Mass Transfer, 111, pp.709–722 (2017)
9. 9.
   Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition

   Gan, Z., Yu, G., He, X. and Li, S.

   International Journal of Heat and Mass Transfer, 104, pp.28–38 (2017)
10. 10.
    Mechanistic Data Science for STEM Education and Applications

    Liu, W.K., Gan, Z. and Fleming, M.

    Springer (2021) — Book

Experience & Education

Teaching

• MFG 598 Multiphysics Digital Twins Spring 2025
• MECH 4395 Artificial Intelligence in Mechanical Engineering Spring 2024
• MECH 5305/6305 Computational Fluid Dynamics Fall 2023
• MECH 4326 Finite Element Analysis Fall 2023
• MECH 5311/6311 Advanced Finite Element Analysis Spring 2023

Research Group

Group members at the synchrotron beamline experiment

Professional Service

• Co-Chair, SIM-AM 2025
• Session Organizer, ASME IMECE 2025
• Session Organizer, USNCCM18
• Session Organizer, MS&T24
• Co-Chair, MMLDT-CSET Conferences (2021, 2023)
• Member: ASME, AAAS, USACM