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    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

    • 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.

    • ST0105: Internship - Surrogate Modeling for Sound Propagation

      MERL is seeking a motivated and qualified individual to work on fast surrogate models for sound emission and propagation from complex vibrating structures, with applications in HVAC noise reduction. The ideal candidate will be a PhD student in engineering or related fields with a solid background in frequency-domain acoustic modeling and numerical techniques for partial differential equations (PDEs). Preferred skills include knowledge of the boundary element method (BEM), data-driven modeling, and physics-informed machine learning. Publication of the results obtained during the internship is expected. The duration is expected to be at least 3 months with a flexible start date.

    • EA0073: Internship - Fault Detection for Electric Machines

      MERL is seeking a motivated and qualified individual to conduct research on electric machine fault analysis and detection methods. Ideal candidates should be Ph.D. students with a solid background and publication record in one more research area on electric machines: electric and magnetic modeling, machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Knowledge on data analysis and machine learning algorithms, and strong programming skills using Python/PyTorch are expected. Research experience on modeling and analysis of electric machines and fault diagnosis is desired. Senior Ph.D. students in related expertise, such as electrical engineering, mechanical engineering, and applied physics are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.


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  • Recent Publications

    •  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, 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,
      • 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}
      • }
    •  Tang, W.-T., Chakrabarty, A., Paulson, J.A., "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2024-167 PDF
      • @inproceedings{Tang2024dec,
      • author = {Tang, Wei-Ting and Chakrabarty, Ankush and Paulson, Joel A.},
      • title = {{TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions}},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-167}
      • }
    See All Publications for Multi-Physical Modeling

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