MERL - Mitsubishi Electric Research Laboratories

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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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  • Internships

    • MD1593: Design Optimization for Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research on design optimization of electrical machines. The ideal candidate should have solid background and demonstrated research experience in mathematical optimization methods, especially in topology optimization, robust optimization, sensitivity analysis, and machine learning techniques. Hands-on experiences with the implementation of optimization algorithms, machine learning and deep learning methods are highly desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • 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. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • 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. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.


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  • Openings


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  • Recent Publications

    •  Rackauckas, C., Anantharaman, R., Edelman, A., Gowda, S., Gwozdz, M., Jain, A., Laughman, C.R., Ma, Y., Martinuzzi, F., Pal, A., Rajput, U., Saba, E., Shah, V., "Composing Modeling and Simulation with Machine Learning in Julia", 14th International Modelica Conference, September 2021.
      BibTeX TR2021-114 PDF
      • @inproceedings{Rackauckas2021sep,
      • author = {Rackauckas, Chris and Anantharaman, Ranjan and Edelman, Alan and Gowda, Shashi and Gwozdz, Maja and Jain, Anand and Laughman, Christopher R. and Ma, Yingbo and Martinuzzi, Francesco and Pal, Avik and Rajput, Utkarsh and Saba, Elliot and Shah, Viral},
      • title = {Composing Modeling and Simulation with Machine Learning in Julia},
      • booktitle = {14th International Modelica Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-114}
      • }
    •  Chakrabarty, A., Maddalena, E., Qiao, H., Laughman, C.R., "Data-driven calibration of physics-informed models of joint building/equipment dynamics using Bayesian optimization", 2021 Building Simulation Conference, September 2021.
      BibTeX TR2021-105 PDF Video
      • @inproceedings{Chakrabarty2021sep,
      • author = {Chakrabarty, Ankush and Maddalena, Emilio and Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Data-driven calibration of physics-informed models of joint building/equipment dynamics using Bayesian optimization},
      • booktitle = {2021 Building Simulation Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-105}
      • }
    •  Tanaka, R., Nabi, S., Nonaka, M., "Airflow Optimization for Room Air Conditioners", Building Simulation 2021 Conference, September 2021.
      BibTeX TR2021-106 PDF
      • @inproceedings{Tanaka2021sep,
      • author = {Tanaka, Ryuta and Nabi, Saleh and Nonaka, Mio},
      • title = {Airflow Optimization for Room Air Conditioners},
      • booktitle = {Building Simulation 2021 Conference},
      • year = 2021,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2021-106}
      • }
    •  Qiao, H., Laughman, C.R., "Modeling and Analysis of Pressure Drop Oscillations in Horizontal Boiling Flow", International Refrigeration and Air Conditioning Conference at Purdue, August 2021.
      BibTeX TR2021-102 PDF
      • @inproceedings{Qiao2021aug,
      • author = {Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Modeling and Analysis of Pressure Drop Oscillations in Horizontal Boiling Flow},
      • booktitle = {International Refrigeration and Air Conditioning Conference at Purdue},
      • year = 2021,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2021-102}
      • }
    •  Chakrabarty, A., Wichern, G., Laughman, C.R., "ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins", International Conference on Machine Learning (ICML), July 2021.
      BibTeX TR2021-086 PDF
      • @inproceedings{Chakrabarty2021jul,
      • author = {Chakrabarty, Ankush and Wichern, Gordon and Laughman, Christopher R.},
      • title = {ANP-BBO: Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2021,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2021-086}
      • }
    •  Zhang, S., Ye, F., Wang, B., Habetler, T.G., "Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning", IEEE Transactions on Industry Applications, DOI: 10.1109/​TIA.2021.3091958, June 2021.
      BibTeX TR2021-081 PDF
      • @article{Zhang2021jun,
      • author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
      • title = {Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning},
      • journal = {IEEE Transactions on Industry Applications},
      • year = 2021,
      • month = jun,
      • doi = {10.1109/TIA.2021.3091958},
      • url = {https://www.merl.com/publications/TR2021-081}
      • }
    •  Laughman, C.R., Qiao, H., "Patch-based Thermodynamic Property Models for the Subcritical Region", Purdue Air-Conditioning and Refrigeration Conference, May 2021.
      BibTeX TR2021-053 PDF
      • @inproceedings{Laughman2021may,
      • author = {Laughman, Christopher R. and Qiao, Hongtao},
      • title = {Patch-based Thermodynamic Property Models for the Subcritical Region},
      • booktitle = {Purdue Air-Conditioning and Refrigeration Conference},
      • year = 2021,
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-053}
      • }
    •  Zhou, L., Guo, F., Wang, H., Wang, B., "High-Torque Direct-Drive Machine with Combined Axial- and Radial-flux Out-runner Vernier Permanent Magnet Motor", International Electric Machine & Drives Conference (IEMDC), DOI: 10.1109/​IEMDC47953.2021.9449499, May 2021, pp. 1-8.
      BibTeX TR2021-050 PDF
      • @inproceedings{Zhou2021may,
      • author = {Zhou, Lei and Guo, Feng and Wang, Hongyu and Wang, Bingnan},
      • title = {High-Torque Direct-Drive Machine with Combined Axial- and Radial-flux Out-runner Vernier Permanent Magnet Motor},
      • booktitle = {2021 IEEE International Electric Machines Drives Conference (IEMDC)},
      • year = 2021,
      • pages = {1--8},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.1109/IEMDC47953.2021.9449499},
      • url = {https://www.merl.com/publications/TR2021-050}
      • }
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  • Videos