Personal profile
Research interests
- Multiscale multiphysics modeling and simulation
- Physics-informed machine learning
- Optimization and uncertainty quantification
- Process monitoring and control
Biography
Dehao Liu received his BS (2016) in mechanical engineering from Tsinghua University in China and his PhD (2021) in mechanical engineering from Georgia Institute of Technology. Before joining Binghamton University in January 2022, he was appointed as a postdoctoral research researcher (2021) at Texas A&M University.
His research focuses on constructing comprehensive and robust process-structure-property relationships for systematic process and materials design for advanced manufacturing. His current research interests include multiscale multiphysics modeling and simulation, physics-informed machine learning, optimization and uncertainty quantification, and process monitoring and control.
Related documents
Education/Academic qualification
Bachelor, Tsinghua University
PhD, Georgia Institute of Technology
ASJC Scopus Subject Areas
- Mechanical Engineering
Researcher Selected Keywords
- Multiscale multiphysics modeling and simulation
- Physics-informed machine learning
- Optimization and uncertainty quantification
- Process monitoring and control
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Collaborations and top research areas from the last five years
Grants & Projects
- 1 Active
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3D-Printed Stainless Steel Electrodes for Advancing Mems Microbial Fuel Cells Toward Sustainable on-chip Energy
Elhadad, A., Li, G., Yang, J., Liu, D. & Choi, S., 2025, 2025 IEEE 38th International Conference on Micro Electro Mechanical Systems, MEMS 2025. Institute of Electrical and Electronics Engineers Inc., p. 643-646 4 p. (Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
1 Scopus citations -
Effects of processing parameters on joining strength of 316L-Cu interface in multi-materials laser powder bed fusion
Yang, J. & Liu, D., Aug 2025, In: Manufacturing Letters. 44, p. 784-791 8 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning
Xu, L., Wang, Z., Rodgers, T., Liu, D., Tran, A. & Xu, H., May 1 2025, In: Acta Materialia. 289, 120859.Research output: Contribution to journal › Article › peer-review
Open Access1 Scopus citations -
Finite-Volume Physics-Informed U-Net for Flow Field Reconstruction With Sparse Data
Zhu, T., Liu, D. & Lu, Y., Jul 1 2025, In: Journal of Computing and Information Science in Engineering. 25, 7, 071004.Research output: Contribution to journal › Article › peer-review
Open Access5 Scopus citations -
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
Hoffman, N., Diniz, C., Liu, D., Rodgers, T., Tran, A. & Fuge, M., Apr 15 2025, In: Acta Materialia. 288, 120784.Research output: Contribution to journal › Article › peer-review
Open Access6 Scopus citations