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 to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
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    •  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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  • Internships

    • MS0110: Internship - Stochastic MPC for Grid-Interactive Buildings and HVAC

      MERL is looking for a highly motivated and qualified candidate to work on stochastic control for grid-interactive net-zero energy buildings informed by deep generative models. The ideal candidate will have a strong understanding of optimization-based control with expertise demonstrated via, e.g., publications, in stochastic model predictive control.

      Additional understanding of energy systems and machine learning is a plus. Hands-on programming experience with numerical optimization solvers and Python fluency is required. The results of this 3-6 month internship are expected to be published in top-tier energy systems and/or control venues.

    • 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.

    • 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.


    See All Internships for Multi-Physical Modeling

  • Recent Publications

    •  Tang, W.-T., Chakrabarty, A., Paulson, J.A., "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2024-167 PDF
      • @inproceedings{Tang2024dec,
      • author = {Tang, Wei-Ting and Chakrabarty, Ankush and Paulson, Joel A.}},
      • title = {TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-167}
      • }
    •  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}
      • }
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