Multiscale Systems Engineering Laboratory


Education

  • Ph.D., University of Pittsburgh, 2003

Teaching Interests

Professor Wang’s teaching interests encompass core mechanical engineering subjects at both undergraduate and graduate levels, including numerical methods, computer-aided design, machine learning, and capstone design. He emphasizes hands-on and project-based learning, and includes laboratory components and course projects to enhance the comprehension of concepts and practical skills. His teaching approach fosters interdisciplinary understanding and prepares students for advanced study and research in mechanical engineering and related fields.

Research Interests

Professor Wang’s research centers on computational methods for design, manufacturing, and materials, particularly modeling and simulation, design optimization, uncertainty quantification, physics-informed machine learning, and quantum scientific computing. His current work focuses on developing new quantum computing algorithms and approaches to perform simulation and optimization on quantum computers and address the scalability challenges in quantum engineering.

Recent Publications

  • Kim J. E. and Wang Y. (2025) Variational quantum algorithm for constrained topology optimization. Quantum Science and Technology, 10(4): 045025.
  • Sul J. and Wang Y. (2025) Generic and scalable differential-equation solver for quantum scientific computing. Physical Review A, 111: 012625.
  • Kim J. E. and Wang, Y. (2023) Quantum approximate Bayesian optimization algorithms with two mixers and uncertainty quantification. IEEE Transactions on Quantum Engineering, 4:3102817.
  • Wang Y., Kim J. E., and Suresh, K. (2023) Opportunities and challenges of quantum computing for engineering optimization. Journal of Computing and Information Science in Engineering, 23(6): 060817.
  • Wang Y., Tran A., and McDowell D.L. (2025) Fundamentals of Uncertainty Quantification for Engineers: Methods and Models. Elsevier. (ISBN:9780443136610, 434 pages).