Education

  • Ph.D., Nuclear Engineering, Massachusetts Institute of Technology, 2021
  • B.S., Nuclear Science and Technology, University of Science and Technology of China, 2015

Teaching Interests

Professor Che’s teaching interests center on core nuclear engineering subjects at both undergraduate and graduate levels. These include fundamentals of nuclear science and engineering, reactor engineering, and AI/ML application in nuclear energy systems. She emphasizes integrating theoretical principles with practical engineering systems to enhance student understanding and problem-solving abilities within nuclear energy systems.

Research Interests

Professor Che’s research focuses on multiphysics modeling & simulation (M&S) of advanced nuclear reactors, with a special emphasis on nuclear fuel performance. Her research group also develops and integrates Artificial Intelligence (AI) and Machine Learning (ML) algorithms into M&S of nuclear reactors, focusing on multiscale model development, multiphysics coupling, and design optimization.

Recent Publications

  • Y Che, OW Calvin, Y Wang, SLN Dhulipala, P Balestra, J Ortensi, Reduced-order modeling for efficient cross section library development in high-temperature gas reactor pebble-bed depletion analysis, Annals of Nuclear Energy 227, 111956, 2026.
  • PCA Simon, CT Icenhour, G Singh, AD Lindsay, C Bhave, L Yang, A Riet, ..., MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8) for advanced open-source tritium transport and fuel cycle modeling, Fusion Engineering and Design 214, 114874, 2025.
  • A Halimi, Y Che, K Shirvan, Design and full core fuel performance assessment of high burnup cores for 4-loop PWRs, Progress in Nuclear Energy 186, 105791, 2025.
  • OW Calvin, Y Che, Y Wang, P Balestra, J Ortensi, Deployment of neural-network-based neutron microscopic cross sections in the Griffin reactor physics application, Annals of Nuclear Energy 220, 111509, 2025.
  • SLN Dhulipala, P German, Y Che, ZM Prince, X Xie, PCA Simon, ..., MOOSE ProbML: Parallelized probabilistic machine learning and uncertainty quantification for computational energy applications, Journal of Computational Science, 102776, 2025.