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.

  • 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

    • MS0109: Internship - Time-Series Forecasting for Energy Systems

      MERL seeks graduate students passionate about deep learning and energy systems to contribute to the development of deep time-series forecasting models for real building energy data. The work will involve multi-domain research including deep learning model development, time-series analysis, and possibly integration with energy management systems. The methods will be implemented and evaluated using real-world datasets. The results of the internship are expected to be published in top-tier machine learning and energy systems conferences and/or journals.

      Exact start date is flexible (most likely Summer 2025), with an expected duration of 3-6 months, depending on agreed scope and intermediate progress.

      Required Specific Experience:

      • Current or past enrollment in a PhD program in Electrical Engineering, Computer Science, or a related field with a focus on Machine Learning or Energy Systems.
      • 2+ years of research experience in at least some of the following areas: deep learning, time-series analysis, probabilistic machine learning, energy systems modeling.
      • PyTorch fluency.
      • Familiarity with real-world data wrangling.
      • Experience with time-series data visualization and analysis tools.

      Strong Pluses:

      • Familiarity with transformer-based time-series forecasting methodologies e.g. TFT or time-series foundation models.
      • Familiarity with adaptation mechanisms e.g. fine-tuning, meta-learning.

    • MS0098: Internship - Control and Estimation for Large=Scale Thermofluid Systems

      MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.

    • MS0102: Internship - Estimation and Calibration of Multi-physical Systems Using Experiments

      MERL is looking for a highly motivated and qualified candidate to work on estimation and calibration of muti-physical systems governed by differential algebraic equations (DAEs). The research will involve study, development and efficient implementation of estimation/calibration approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited experimental data. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, optimization, and model calibration; with expertise demonstrated via, e.g., peer-reviewed publications. Prior experience in working with experimental data, and programming in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months, and the start date is flexible.

      Required Specific Experience

      • Graduate student with 2+ years of relevant research experience

      Additional Desired Experience

      • Strong programming skills in Julia or Modelica
      • Prior experience in working with thermofluid systems
      • Prior experience in estimation/calibration of complex nonlinear systems using experimental data

      Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

      MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.

      Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation. MERL may delay commencement of employment, rescind an offer of employment, terminate employment, and/or modify job responsibilities, compensation, benefits, and/or access to MERL facilities and information systems, as MERL deems appropriate, to ensure practical compliance with applicable employment law and government access control restrictions.


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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}
      • }
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  • Videos