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 researchers present 9 papers at ACC 2024
      Date: July 10, 2024 - July 12, 2024
      Where: Toronto, Canada
      MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
      Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.

        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, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
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    •  NEWS    Ankush Chakrabarty gave a lecture at UT-Austin's Seminar Series on Occupant-Centric Grid-Interactive Buildings
      Date: March 20, 2024
      Where: Austin, TX
      MERL Contact: Ankush Chakrabarty
      Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.

        The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
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  • Internships

    • 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.

    • EA0072: Internship - Electric Machine Topology Optimization

      MERL is seeking a motivated and qualified intern to conduct research on shape and topology optimization of electrical machines. The ideal candidate should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, and sensitivity analysis, as well as machine learning methods. Hands-on coding experience with the implementation of topology optimization algorithms and finite-element simulation are 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 around 3 months.


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  • Recent Publications

    •  Xiang, X., Palash, R., Yagyu, E., Dunham, S., Teo, K.H., Chowdhury, N., "AI-assisted Field Plate Design of GaN HEMT Device", Advanced Theory and Simulation, October 2024.
      BibTeX TR2024-152 PDF
      • @article{Xiang2024oct,
      • author = {Xiang, Xiaofeng and Palash, Rafid and Yagyu, Eiji and Dunham, Scott and Teo, Koon Hoo and Chowdhury, Nadim}},
      • title = {AI-assisted Field Plate Design of GaN HEMT Device},
      • journal = {Advanced Theory and Simulation},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-152}
      • }
    •  Bortoff, S.A., Laughman, C.R., Deshpande, V.M., Qiao, H., "Fluid Property Functions in Polar and Parabolic Coordinates", American Modelica Conference, October 2024.
      BibTeX TR2024-144 PDF
      • @inproceedings{Bortoff2024oct,
      • author = {Bortoff, Scott A. and Laughman, Christopher R. and Deshpande, Vedang M. and Qiao, Hongtao}},
      • title = {Fluid Property Functions in Polar and Parabolic Coordinates},
      • booktitle = {American Modelica Conference},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-144}
      • }
    •  Vanfretti, L., Laughman, C.R., Chakrabarty, A., "Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation", American Modelica Conference, October 2024.
      BibTeX TR2024-140 PDF
      • @inproceedings{Vanfretti2024oct,
      • author = {Vanfretti, Luigi and Laughman, Christopher R. and Chakrabarty, Ankush}},
      • title = {Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation},
      • booktitle = {American Modelica Conference},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-140}
      • }
    •  Zhang, H., Wang, B., "Supervised Contrastive Learning for Electric Motor Bearing Fault Detection", International Conference on Electrical Machines (ICEM), September 2024.
      BibTeX TR2024-120 PDF
      • @inproceedings{Zhang2024sep,
      • author = {Zhang, Hengrui and Wang, Bingnan}},
      • title = {Supervised Contrastive Learning for Electric Motor Bearing Fault Detection},
      • booktitle = {International Conference on Electrical Machines (ICEM)},
      • year = 2024,
      • month = sep,
      • url = {https://www.merl.com/publications/TR2024-120}
      • }
    •  Chakrabarty, A., Vanfretti, L., Bortoff, S.A., Deshpande, V.M., Wang, Y., Paulson, J.A., Zhan, S., Laughman, C.R., "Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks", IEEE Conference on Control Technology and Applications (CCTA) 2024, DOI: 10.1109/​CCTA60707.2024.10666585, August 2024.
      BibTeX TR2024-113 PDF
      • @inproceedings{Chakrabarty2024aug,
      • author = {Chakrabarty, Ankush and Vanfretti, Luigi and Bortoff, Scott A. and Deshpande, Vedang M. and Wang, Ye and Paulson, Joel A. and Zhan, Sicheng and Laughman, Christopher R.}},
      • title = {Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
      • year = 2024,
      • month = aug,
      • doi = {10.1109/CCTA60707.2024.10666585},
      • url = {https://www.merl.com/publications/TR2024-113}
      • }
    •  Park, S., Wang, Y., Qiao, H., Sakamoto, Y., Wang, B., Liu, D., "Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics", IEEE Conference on Control Technology and Applications (CCTA) 2024, DOI: 10.1109/​CCTA60707.2024.10666575, August 2024.
      BibTeX TR2024-114 PDF
      • @inproceedings{Park2024aug,
      • author = {Park, Seho and Wang, Yebin and Qiao, Hongtao and Sakamoto, Yusuke and Wang, Bingnan and Liu, Dehong}},
      • title = {Control Co-Design for Electric Vehicles with Driving Cycle Synthesis Encoding Road Traffic and Driver Characteristics},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
      • year = 2024,
      • month = aug,
      • doi = {10.1109/CCTA60707.2024.10666575},
      • url = {https://www.merl.com/publications/TR2024-114}
      • }
    •  Bortoff, S.A., Qiao, H., Laughman, C.R., "Modeling and Control of a Multi-Mode Heat Pump", IEEE Conference on Control Technology and Applications (CCTA) 2024, August 2024.
      BibTeX TR2024-111 PDF
      • @inproceedings{Bortoff2024aug,
      • author = {{Bortoff, Scott A. and Qiao, Hongtao and Laughman, Christopher R.}},
      • title = {Modeling and Control of a Multi-Mode Heat Pump},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
      • year = 2024,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2024-111}
      • }
    •  Vanfretti, L., Laughman, C.R., "Power System Modeling for Identification and Control Applications using Modelica and OpenIPSL", Conference on Control Technology and Applications (CCTA), August 2024.
      BibTeX TR2024-112 PDF
      • @inproceedings{Vanfretti2024aug,
      • author = {{Vanfretti, Luigi and Laughman, Christopher R.}},
      • title = {Power System Modeling for Identification and Control Applications using Modelica and OpenIPSL},
      • booktitle = {Conference on Control Technology and Applications (CCTA)},
      • year = 2024,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2024-112}
      • }
    See All Publications for Multi-Physical Modeling

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