Principal Investigator

Ye Lu

Assistant Professor of Mechanical Engineering
University of Maryland Baltimore County
Phone: 410-455-2801


Ph.D., INSA Lyon, University of Lyon, France, 2017
M.S. and Diplôme d’Ingénieur, INSA Lyon, France, 2014
B.S., Northwestern Polytechnical University, China, 2012


Assistant Professor, University of Maryland Baltimore County, 2022-Present
Postdoc, Northwestern University, 2019-2022
Postdoc, CEA Cadarache, 2018-2019

Research Interests

  • Computational mechanics
  • Reduced order modeling and scientific data-driven techniques
  • Multi-scale modeling of materials and manufacturing processes, including welding and additive manufacturing (AM)
  • AM-oriented topology optimization for integrated materials and process design
  • Digital image correlation techniques

Awards & Honors

UMBC Strategic Awards for Research Transitions (START), 2023
UMBC Summer Research Faculty Fellowship (SURFF), 2023
Three First Place and Two Second Place awards, NIST AM Bench challenges, 2022
Top-Performer, Additive Manufacturing Modeling Challenge Series, America Makes and Air Force Research Laboratory (AFRL), 2020

Selected Publications

Y. Lu, W. Zhu, Convolution finite element based digital image correlation for displacement and strain measurements. Computer Methods in Applied Mechanics and Engineering, 2024.
Y. Lu, S. Mojumder, J. Guo, Y. Li, W. K. Liu, Extended tensor decomposition model reduction methods: Training, prediction, and design under uncertainty, Computer Methods in Applied Mechanics and Engineering, 2024.
Y. Lu, H. Li, L. Zhang, et al. Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond. Comput Mech, 2023.
H. Li, S. Knapik, Y. Li, C. Park, J. Guo, S. Mojumder, Y. Lu, W. Chen, D. W Apley, W. K. Liu, Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C-HiDeNN-TD) for high-resolution topology optimization, Comput Mech, 2023.
Y. Lu, H. Li, S. Saha, S. Mojumder, A. A. Amin, D. Suarez, Y. Liu, D. Qian, and W. K. Liu, Reduced order machine learning finite element methods: concept, implementation, and future applications, Computer Modeling in Engineering & Science, 2021.
Y. Lu, K. K. Jones, Z. Gan, and W. K. Liu, Adaptive hyper reduction for additive manufacturing thermal fluid analysis, Computer Methods in Applied Mechanics and Engineering, 2020.
Y. Lu, T. Helfer, B. Bary, and O. Fandeur, An efficient and robust staggered algorithm applied to the quasi-static description of brittle fracture by a phase-field approach, Computer Methods in Applied Mechanics and Engineering, 2020.
Y. Lu, N. Blal, and A. Gravouil, Datadriven HOPGD based computational vademecum for welding parameters identification, Computational Mechanics, 2019.
Y. Lu, N. Blal, and A. Gravouil, Adaptive sparse grid based HOPGD: Towards a nonintrusive strategy for constructing space-time welding computational vademecum, International Journal for Numerical Methods in Engineering, 2018.
Y. Lu, N. Blal, and A. Gravouil, Space–time POD based computational vademecums for parametric studies: application to thermo-mechanical problems, Advanced Modeling and Simulation in Engineering Sciences, 2018.

Full Publication List

Google Scholar



ENME 687, Finite Element Methods: Fundamentals and Applications, Fall 2024
ENME 489, Intro to Finite Element Methods, Fall 2024
ENME 684, Mechanical Behavior of Materials, Fall 2023
ENME 220, Mechanics of Materials, Spring 2023, Fall 2023


American Society of Mechanical Engineers
United States Association for Computational Mechanics
International Association for Computational Mechanics