MERL - Mitsubishi Electric Research Laboratories

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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  • News & Events

    •  TALK   [MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection
      Date & Time: Tuesday, December 14, 2021; 1:00 PM EST
      Speaker: Prof. Chris Fletcher, University of Waterloo
      MERL Host: Ankush Chakrabarty
      Research Areas: Dynamical Systems, Machine Learning, Multi-Physical Modeling
      Brief
      • Decision-making and adaptation to climate change requires quantitative projections of the physical climate system and an accurate understanding of the uncertainty in those projections. Earth system models (ESMs), which solve the Navier-Stokes equations on the sphere, are the only tool that climate scientists have to make projections forward into climate states that have not been observed in the historical data record. Yet, ESMs are incredibly complex and expensive codes and contain many poorly constrained physical parameters—for processes such as clouds and convection—that must be calibrated against observations. In this talk, I will describe research from my group that uses ensembles of ESM simulations to train statistical models that learn the behavior and sensitivities of the ESM. Once trained and validated the statistical models are essentially free to run, which allows climate modelling centers to make more efficient use of precious compute cycles. The aim is to improve the quality of future climate projections, by producing better calibrated ESMs, and to improve the quantification of the uncertainties, by better sampling the equifinality of climate states.
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    •  EVENT   Prof. Melanie Zeilinger of ETH to give keynote at MERL's Virtual Open House
      Date & Time: Thursday, December 9, 2021; 1:00pm - 5:30pm EST
      Speaker: Prof. Melanie Zeilinger, ETH
      Location: Virtual Event
      Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Digital Video, Human-Computer Interaction, Information Security
      Brief
      • MERL is excited to announce the second keynote speaker for our Virtual Open House 2021:
        Prof. Melanie Zeilinger from ETH .

        Our virtual open house will take place on December 9, 2021, 1:00pm - 5:30pm (EST).

        Join us to learn more about who we are, what we do, and discuss our internship and employment opportunities. Prof. Zeilinger's talk is scheduled for 3:15pm - 3:45pm (EST).

        Registration: https://mailchi.mp/merl/merlvoh2021

        Keynote Title: Control Meets Learning - On Performance, Safety and User Interaction

        Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.
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  • Internships

    • MD1693: Aircraft electric propulsion system design

      MERL is seeking a motivated and qualified individual to conduct research in modeling, simulation and analysis of aircraft electric propulsion system. The ideal candidate should have solid backgrounds in multi-physics modeling and simulation of aircraft electrical propulsion system. Demonstrated experience in modeling and simulation software/language such as Modelica or Simscape is a necessity. Knowledge and experience of NPSS, aircraft dynamics, and aerodynamics is a definite plus. Senior Ph.D. students in aerospace and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.

    • MD1714: Electric Motor Design

      MERL is seeing a motivated and qualified individual to conduct research on electric machine design, prototype, and experiment tests. The ideal candidate should have solid background and demonstrated research experience in electric machine theory, design analysis, motor drives, and control. Hands-on experiences on electric motor design and prototyping, test bench set up, and experiment measurements are required. Senior Ph.D. students in electrical engineering or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible. This internship requires work that can only be done at MERL.

    • MD1697: Integrated design of mechatronic systems

      MERL is seeking a highly motivated and qualified individual to conduct research in model-based mechatronic system design. The ideal candidate should have solid backgrounds in motor and drives, multi-body dynamics, design optimization, and coding skills. Demonstrated experience on hand-on mechatronic system integration, and simulation/optimization software such as Matlab is a necessity. Ph.D. students in mechanical engineering, robotics, and electrical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.


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  • Openings


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  • Recent Publications

    •  Jeon, W., Chakrabarty, A., Zemouche, A., Rajamani, R., "Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications", IEEE/ASME Transactions on Mechatronics, January 2022.
      BibTeX TR2022-003 PDF
      • @article{Jeon2022jan,
      • author = {Jeon, Woongsun and Chakrabarty, Ankush and Zemouche, Ali and Rajamani, Rajesh},
      • title = {Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications},
      • journal = {IEEE/ASME Transactions on Mechatronics},
      • year = 2022,
      • month = jan,
      • url = {https://www.merl.com/publications/TR2022-003}
      • }
    •  Teo, K.H., Zhang, Y., Chowdhury, N., Rakheja, S., Ma, R., Xie, Q., Yagyu, E., Yamanaka, K., Li, K., Palacios, T., "Emerging GaN technologies for power, RF, digital and quantum computing applications: recent advances and prospects", Journal of Applied Physics, DOI: 10.1063/​5.0061555, December 2021.
      BibTeX TR2022-002 PDF
      • @article{Teo2021dec,
      • author = {Teo, Koon Hoo and Zhang, Yuhao and Chowdhury, Nadim and Rakheja, Shaloo and Ma, Rui and Xie, Qingyun and Yagyu, Eiji and Yamanaka, Koji and Li, Kexin and Palacios, Tomas},
      • title = {Emerging GaN technologies for power, RF, digital and quantum computing applications: recent advances and prospects},
      • journal = {Journal of Applied Physics},
      • year = 2021,
      • month = dec,
      • doi = {10.1063/5.0061555},
      • url = {https://www.merl.com/publications/TR2022-002}
      • }
    •  Zhan, S., Wichern, G., Laughman, C.R., Chakrabarty, A., "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.
      BibTeX TR2021-149 PDF
      • @inproceedings{Zhan2021dec,
      • author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chakrabarty, Ankush},
      • title = {Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-149}
      • }
    •  Wang, B., Zhou, L., Miyoshi, M., Inoue, H., Kanemaru, M., "Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.23919/​ICEMS52562.2021.9634328, November 2021, pp. 944-948.
      BibTeX TR2021-139 PDF
      • @inproceedings{Wang2021nov3,
      • author = {Wang, Bingnan and Zhou, Lei and Miyoshi, Masahito and Inoue, hiroshi and Kanemaru, Makoto},
      • title = {Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory},
      • booktitle = {2021 24th International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2021,
      • pages = {944--948},
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.23919/ICEMS52562.2021.9634328},
      • url = {https://www.merl.com/publications/TR2021-139}
      • }
    •  Chakrabarty, A., Bortoff, S.A., Laughman, C.R., "Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters", IEEE International Conference on Systems, Man, and Cybernetics (SMC), DOI: 10.1109/​SMC52423.2021.9658893, October 2021.
      BibTeX TR2021-128 PDF Video
      • @inproceedings{Chakrabarty2021oct2,
      • author = {Chakrabarty, Ankush and Bortoff, Scott A. and Laughman, Christopher R.},
      • title = {Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters},
      • booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
      • year = 2021,
      • month = oct,
      • doi = {10.1109/SMC52423.2021.9658893},
      • url = {https://www.merl.com/publications/TR2021-128}
      • }
    •  Wang, B., Shin, K.-H., Hidaka, Y., Kondo, S., Arita, H., Ito, K., "Analytical Magnetic Model for Variable-Flux Interior Permanent Magnet Synchronous Motors", IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/​ECCE47101.2021.9595341, October 2021, pp. 4142-4148.
      BibTeX TR2021-123 PDF
      • @inproceedings{Wang2021oct2,
      • author = {Wang, Bingnan and Shin, Kyung-Hun and Hidaka, Yuki and Kondo, Shota and Arita, Hideaki and Ito, Kazumasa},
      • title = {Analytical Magnetic Model for Variable-Flux Interior Permanent Magnet Synchronous Motors},
      • booktitle = {2021 IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2021,
      • pages = {4142--4148},
      • month = oct,
      • publisher = {IEEE},
      • doi = {10.1109/ECCE47101.2021.9595341},
      • url = {https://www.merl.com/publications/TR2021-123}
      • }
    •  Wang, B., Zhou, L., Wang, H., Lin, C., "Analytical Modeling and Design Optimization of a Vernier Permanent Magnet Motor", IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/​ECCE47101.2021.9595230, October 2021, pp. 4480-4485.
      BibTeX TR2021-124 PDF
      • @inproceedings{Wang2021oct3,
      • author = {Wang, Bingnan and Zhou, Lei and Wang, Hongyu and Lin, Chungwei},
      • title = {Analytical Modeling and Design Optimization of a Vernier Permanent Magnet Motor},
      • booktitle = {2021 IEEE Energy Conversion Congress and Exposition (ECCE)},
      • year = 2021,
      • pages = {4480--4485},
      • month = oct,
      • publisher = {IEEE},
      • doi = {10.1109/ECCE47101.2021.9595230},
      • url = {https://www.merl.com/publications/TR2021-124}
      • }
    •  Leonard, E., Qiao, H., Nabi, S., "A Comparison of Interpolation Methods in Fast Fluid Dynamics", International High Performance Buildings Conference, September 2021.
      BibTeX TR2021-118 PDF
      • @inproceedings{Leonard2021sep,
      • author = {Leonard, Eric and Qiao, Hongtao and Nabi, Saleh},
      • title = {A Comparison of Interpolation Methods in Fast Fluid Dynamics},
      • booktitle = {International High Performance Buildings Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-118}
      • }
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  • Videos