MERL - Mitsubishi Electric Research Laboratories

Share:

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.

  • Quick Links

  • Researchers

  • News & Events


    See All News & Events for Multi-Physical Modeling

  • Internships

    • MD1381: Electric Motor Design

      MERL is seeking a motivated and qualified individual to conduct research in design, modeling, and simulation of electrical machines. The ideal candidate should have solid backgrounds in modeling (including model reduction)/co-simulation of electromagnetics and thermal dynamics of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and real-time control experiments involving motor drives is a strong plus. Senior Ph.D. students in electrical or mechanical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months.

    • MD1558: Symbolic regression

      MERL is seeking a self-motivated intern to conduct fundamental research in the area of symbolic regression and deep learning for applications of recovering mathematical expressions or physical laws. The ideal candidate would be a senior PhD student with solid background in machine learning and strong publication record in top-tier venues. Prior experience in symbolic regression is strongly preferred. Very good Python, Pytorch/Tensorflow, and Matlab skills are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    • 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.


    See All Internships for Multi-Physical Modeling

  • Recent Publications

    •  Bortoff, S.A., Okasha, A., "Modelica-Based Control of A Delta Robot", ASME Dynamic Systems and Control Conference, December 2020.
      BibTeX TR2020-154 PDF
      • @inproceedings{Bortoff2020dec,
      • author = {Bortoff, Scott A. and Okasha, Ahmed},
      • title = {Modelica-Based Control of A Delta Robot},
      • booktitle = {ASME Dynamic Systems and Control Conference},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-154}
      • }
    •  Anantharaman, R., Ma, Y., Gowda, S., Laughman, C.R., Shah, V., Edelman, A., Rackauckas, C., "Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks", Advances in Neural Information Processing Systems (NeurIPS), December 2020.
      BibTeX TR2020-169 PDF
      • @inproceedings{Anantharaman2020dec,
      • author = {Anantharaman, Ranjan and Ma, Yingbo and Gowda, Shashi and Laughman, Christopher R. and Shah, Viral and Edelman, Alan and Rackauckas, Chris},
      • title = {Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2020,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2020-169}
      • }
    •  Talreja, V., Koike-Akino, T., Wang, Y., Millar, D.S., Kojima, K., Parsons, K., "End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping", European Conference on Optical Communication (ECOC), November 2020.
      BibTeX TR2020-155 PDF Video
      • @inproceedings{Talreja2020nov,
      • author = {Talreja, Veeru and Koike-Akino, Toshiaki and Wang, Ye and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
      • title = {End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping},
      • booktitle = {European Conference on Optical Communication (ECOC)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-155}
      • }
    •  Shin, K.-H., Wang, B., "Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core", International Conference on Electrical Machines and Systems (ICEMS), November 2020.
      BibTeX TR2020-149 PDF
      • @inproceedings{Shin2020nov,
      • author = {Shin, Kyung-Hun and Wang, Bingnan},
      • title = {Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core},
      • booktitle = {International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-149}
      • }
    •  Zhang, S., Ye, F., Wang, B., Habetler, T.G., "Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning", International Conference on Electrical Machines and Systems (ICEMS), November 2020.
      BibTeX TR2020-151 PDF
      • @inproceedings{Zhang2020nov2,
      • author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
      • title = {Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning},
      • booktitle = {International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-151}
      • }
    •  Wang, B., Hotta, A., "Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), November 2020.
      BibTeX TR2020-148 PDF
      • @inproceedings{Wang2020nov2,
      • author = {Wang, Bingnan and Hotta, Akira},
      • title = {Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection},
      • booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
      • year = 2020,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2020-148}
      • }
    •  Bhamidipati, S., Kim, K.J., Sun, H., Orlik, P.V., "Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems", IEEE Network, DOI: 10.1109/MNET.011.1900322, Vol. 34, No. 3, pp. 64-72, May 2020.
      BibTeX TR2020-058 PDF
      • @article{Bhamidipati2020may,
      • author = {Bhamidipati, Sriramya and Kim, Kyeong Jin and Sun, Hongbo and Orlik, Philip V.},
      • title = {Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems},
      • journal = {IEEE Network},
      • year = 2020,
      • volume = 34,
      • number = 3,
      • pages = {64--72},
      • month = may,
      • doi = {10.1109/MNET.011.1900322},
      • url = {https://www.merl.com/publications/TR2020-058}
      • }
    •  Zhang, S., Wang, B., Kanemaru, M., Lin, C., Liu, D., Habetler, T., "Model-Based Analysis and Quantification of Bearing Faults in Induction Machines", IEEE Transactions on Industry Applications, DOI: 10.1109/TIA.2020.2979383, Vol. 56, No. 3, pp. 2158-2170, May 2020.
      BibTeX TR2020-059 PDF
      • @article{Zhang2020may,
      • author = {Zhang, Shen and Wang, Bingnan and Kanemaru, Makoto and Lin, Chungwei and Liu, Dehong and Habetler, Thomas},
      • title = {Model-Based Analysis and Quantification of Bearing Faults in Induction Machines},
      • journal = {IEEE Transactions on Industry Applications},
      • year = 2020,
      • volume = 56,
      • number = 3,
      • pages = {2158--2170},
      • month = may,
      • doi = {10.1109/TIA.2020.2979383},
      • issn = {1939-9367},
      • url = {https://www.merl.com/publications/TR2020-059}
      • }
    See All Publications for Multi-Physical Modeling

  • Videos