Multi-Physical Modeling

Optimal design & robust control through multi-physical modeling.

Our work involves the development of state-of-art modeling and simulation tools for complex, heterogeneous systems. We apply these models for the optimal design and robust control of a variety of systems including HVAC systems, zero-energy buildings, automobiles, and robotic systems.

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  • Researchers

  • Awards

    •  AWARD    Mitsubishi Electric Team Wins Awards at GalFer Contest
      Date: June 23, 2025
      Awarded to: Bingnan Wang, Tatsuya Yamamoto, Yusuke Sakamoto, Siyuan Sun, Toshiaki Koike-Akino, and Ye Wang
      MERL Contacts: Toshiaki Koike-Akino; Bingnan Wang; Ye Wang
      Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • The MELSUR (Mitsubishi Electric SURrogate) team, consisting of a group of MERL and Mitsubishi Electric researchers, ranked first in two out of three categories in the GalFer Contest.

        The GalFer (Galileo Ferraris) contest aims to compare the accuracy and efficiency of data-driven methodologies for the multi-physics simulation of traction electric machines. A total of 26 teams worldwide participated in the contest, which consists of three categories. The MELSUR team, including MERL staff Bingnan Wang, Toshiaki Koike-Akino, Ye Wang, MERL intern Siyuan Sun, Mitsubishi Electric researchers Tatsuya Yamamoto and Yusuke Sakamoto, ranked first for the category of "Novelty" and "Interpolation". The results were announced during an award ceremony at the COMPUMAG 2025 conference in Naples, Italy.
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    •  AWARD    Best Paper Award at SDEMPED 2023
      Date: August 30, 2023
      Awarded to: Bingnan Wang, Hiroshi Inoue, and Makoto Kanemaru
      MERL Contact: Bingnan Wang
      Research Areas: Applied Physics, Data Analytics, Multi-Physical Modeling
      Brief
      • MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.

        SDEMPED was established as the only international symposium entirely devoted to the diagnostics of electrical machines, power electronics and drives. It is now a regular biennial event. The 14th version, SDEMPED 2023 was held in Chania, Greece from August 28th to 31st, 2023.
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  • News & Events

    •  NEWS    MERL contributes to 2025 European Control Conference
      Date: June 24, 2025 - June 27, 2025
      Where: Thessaloniki
      MERL Contacts: Stefano Di Cairano; Daniel N. Nikovski; Diego Romeres; Yebin Wang
      Research Areas: Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • MERL researchers contributed to both the technical program and workshop organization at the 2025 European Control Conference (ECC), held in Thessaloniki, Greece, from June 24 to 27. ECC is one of the premier conferences in the field of control.

        In the main conference, MERL researchers presented four papers covering a range of topics, including: Representation learning, Motion planning for tractor-trailers, Motion planning for mobile manipulators, Learning high-dimensional dynamical systems, Model learning for robotics.

        Additionally, MERL co-organized a workshop with the University of Padua titled “Reinforcement Learning for Robotic Control: Recent Developments and Open Challenges.” MERL researcher Diego Romeres also delivered an invited talk titled “Human-Robot Collaborative Assembly” in that workshop.
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    •  NEWS    MERL researchers present 7 papers at CDC 2024
      Date: December 16, 2024 - December 19, 2024
      Where: Milan, Italy
      MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Abraham P. Vinod; Avishai Weiss; Gordon Wichern
      Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.

        As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.

        In addition, Ankush Chakrabarty (Principal Research Scientist, Multiphysical Systems Team) was an invited speaker in the pre-conference Workshop on "Learning Dynamics From Data" where he gave a talk on few-shot meta-learning for black-box identification using data from similar systems.
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  • Internships

    • MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems

      MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

    • EA0151: Internship - Physics-informed machine learning

      MERL is looking for a self-motivated intern to work on physics-informed machine learning with application to electric machine condition monitoring and predictive maintenance. The ideal candidate would be a Ph.D. student in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in Python and Matlab is required. The intern is expected to collaborate with MERL researchers to build machine learning model for multi-modal data analysis, prepare technical reports, and draft manuscripts for scientific publications. The total duration is anticipated to be 3-6 months. The start date is flexible. This internship requires work that can only be done at MERL.

    • EA0149: Internship - Electric Motor Design Optimization

      MERL is seeking a motivated and qualified individual to conduct research on physics informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with solid background and proven publication record in one or more of the following research areas: 2D/3D electromagnetic modeling and simulation, analytical modeling methods for electromagnetics and iron losses (e.g. magnetic equivalent circuit), and machine learning-based surrogate modeling. Strong coding skill with ANSYS or open-source FEM software and Python-based learning library is a must and prior experience with running jobs over cluster is a plus. Start date for this internship is flexible and the duration is 3-6 months.

      Required Specific Experience

      • Experience with modeling and simulations for motor design


    See All Internships for Multi-Physical Modeling

  • Recent Publications

    •  Qiao, H., Miyawaki, K., Nishio, J., Laughman, C.R., "Transient Simulation of Refrigerant and Oil Flow Distribution in Air Source Heat Pump Systems", International Conference on Compressor and Refrigeration, July 2025.
      BibTeX TR2025-107 PDF
      • @inproceedings{Qiao2025jul,
      • author = {Qiao, Hongtao and Miyawaki, Kosuke and Nishio, Jun and Laughman, Christopher R.},
      • title = {{Transient Simulation of Refrigerant and Oil Flow Distribution in Air Source Heat Pump Systems}},
      • booktitle = {International Conference on Compressor and Refrigeration},
      • year = 2025,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2025-107}
      • }
    •  Das, G., Wang, B., Lin, C., "Topology Optimization of Electric Motors using Mesh Projection", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
      BibTeX TR2025-089 PDF
      • @inproceedings{Das2025jun2,
      • author = {Das, Ghanendra and Wang, Bingnan and Lin, Chungwei},
      • title = {{Topology Optimization of Electric Motors using Mesh Projection}},
      • booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-089}
      • }
    •  Sun, S., Wang, Y., Koike-Akino, T., Yamamoto, T., Sakamoto, Y., Wang, B., "Image-based Deep Learning Models for Electric Motors", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
      BibTeX TR2025-088 PDF
      • @inproceedings{Sun2025jun,
      • author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
      • title = {{Image-based Deep Learning Models for Electric Motors}},
      • booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-088}
      • }
    •  Ji, D.-Y., Wang, B., Inoue, H., Kanemaru, M., "Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.
      BibTeX TR2025-062 PDF
      • @inproceedings{Ji2025may,
      • author = {Ji, Dai-Yan and Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {{Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method}},
      • booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-062}
      • }
    •  Sun, S., Wang, Y., Koike-Akino, T., Yamamoto, T., Sakamoto, Y., Wang, B., "Electric Motor Cogging Torque Prediction with Vision Transformer Models", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.
      BibTeX TR2025-059 PDF
      • @inproceedings{Sun2025may,
      • author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
      • title = {{Electric Motor Cogging Torque Prediction with Vision Transformer Models}},
      • booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-059}
      • }
    •  Chakrabarty, A., Vanfretti, L., Wang, Y., Mineyuki, T., Zhan, S., Tang, W.-T., Paulson, J.A., Deshpande, V.M., Bortoff, S.A., Laughman, C.R., "Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation", Building Simulation, DOI: 10.1007/​s12273-025-1260-8, April 2025.
      BibTeX TR2025-043 PDF
      • @article{Chakrabarty2025apr,
      • author = {Chakrabarty, Ankush and Vanfretti, Luigi and Wang, Ye and Mineyuki, Takuma and Zhan, Sicheng and Tang, Wei-Ting and Paulson, Joel A. and Deshpande, Vedang M. and Bortoff, Scott A. and Laughman, Christopher R.},
      • title = {{Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation}},
      • journal = {Building Simulation},
      • year = 2025,
      • month = apr,
      • doi = {10.1007/s12273-025-1260-8},
      • url = {https://www.merl.com/publications/TR2025-043}
      • }
    •  Dong, Y., Yagyu, E., Matsuda, T., Teo, K.H., Lin, C., Rakheja, S., "An accurate electrical and thermal co-simulation framework for modeling high-temperature DC and pulsed I-V characteristics of GaN HEMTs", IEEE Journal of the Electron Devices Society, March 2025.
      BibTeX TR2025-041 PDF
      • @article{Dong2025mar,
      • author = {Dong, Yicong and Yagyu, Eiji and Matsuda, Takashi and Teo, Koon Hoo and Lin, Chungwei and Rakheja, Shaloo},
      • title = {{An accurate electrical and thermal co-simulation framework for modeling high-temperature DC and pulsed I-V characteristics of GaN HEMTs}},
      • journal = {IEEE Journal of the Electron Devices Society},
      • year = 2025,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2025-041}
      • }
    •  Park, Y.-J., Germain, F.G., Liu, J., Wang, Y., Koike-Akino, T., Wichern, G., Laughman, C.R., Azizan, N., Chakrabarty, A., "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2025-001 PDF
      • @inproceedings{Park2024dec,
      • author = {{{Park, Young-Jin and Germain, François G and Liu, Jing and Wang, Ye and Koike-Akino, Toshiaki and Wichern, Gordon and Laughman, Christopher R. and Azizan, Navid and Chakrabarty, Ankush}}},
      • title = {{{Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?}}},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2025-001}
      • }
    See All Publications for Multi-Physical Modeling

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