MERL - Mitsubishi Electric Research Laboratories

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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  • News & Events

    •  TALK    [MERL Seminar Series 2022] Albert Benveniste, Benoît Caillaud, and Mathias Malandain present talk titled Exact Structural Analysis of Multimode Modelica Models
      Date & Time: Tuesday, April 5, 2022; 11:00 AM EDT
      Speaker: Albert Benveniste, Benoît Caillaud, and Mathias Malandain, Inria
      MERL Host: Scott A. Bortoff
      Research Areas: Dynamical Systems, Multi-Physical Modeling
      Abstract
      • Since its 3.3 release, Modelica offers the possibility to specify models of dynamical systems with multiple modes having different DAE-based dynamics. However, the handling of such models by the current Modelica tools is not satisfactory, with mathematically sound models yielding exceptions at runtime. In our introduction, will briefly explain why and when the approximate structural analysis implemented in current Modelica tools leads to such errors. Then we will present our multimode Pryce Sigma-method for index reduction, in which the mode-dependent Sigma-matrix is represented in a dual form, by attaching, to every valuation of the sigma_ij entry of the Sigma matrix, the predicate characterizing the set of modes in which sigma_ij takes this value. We will illustrate this multimode analysis on example, by using our IsamDAE tool. In a second part, we will complement this multimode DAE structural analysis by a new structural analysis of mode changes (and, more generally, transient modes holding for zero time). Also, mode changes often give raise to impulsive behaviors: we will present a compile-time analysis identifying such behaviors. Our structural analysis of mode changes deeply relies on nonstandard analysis, which is a mathematical framework in which infinitesimals and infinities are first class citizens.
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    •  TALK    [MERL Seminar Series 2022] Analog CMOS Computing Chips for Fast and Energy-Efficient Solution of PDE Systems
      Date & Time: Tuesday, March 15, 2022; 1:00 PM EDT
      Speaker: Arjuna Madanayake, Florida International University
      MERL Host: Rui Ma
      Research Areas: Applied Physics, Electronic and Photonic Devices, Multi-Physical Modeling
      Abstract
      • Analog computers are making a comeback. In fact, they are taking the world by storm. After decades of “analog computing winter” that followed the invention of the digital computing paradigm in the 1940s, classical physics-based analog computers are being reconsidered for improving the computational throughput of demanding applications. The research is driven by exponential growth in transistor densities and bandwidths in the integrated circuits world, which in turn, has led to new possibilities for the creative circuit designer. Fast analog chips not only furnish communication/radar front-ends, but can also be used to accelerate mathematical operations. Most analog computer today focus on AI and machine learning. E.g., analog in-memory computing plays an exciting role in AI acceleration because linear algebra operations can be mapped efficiently to compute in memory. However, many scientific computing tasks are built on linear and non-linear partial differential equations (PDEs) that require recursive numerical PDE solution across spatial and temporal dimensions. The adoption of analog parallel processors that are built around speed vs power efficiency vs precision trade-offs available from circuitry for PDE solution require new research in computer architecture. We report on recent progress on CMOS based analog computers for solving computational electromagnetics and non-linear pressure wave equations. Our first analog computing chip was measured to be more than 400x faster than a top-of-the-line NVIDIA GPU while consuming 1000x less power for elementary computational electromagnetics computations using finite-difference time-domain scheme.
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  • Internships

    • MS1769: Data-driven Dynamic Modeling of Vapor Compression Systems

      MERL is seeking a highly motivated and qualified individual to conduct research in dynamic modeling and simulation of vapor compression systems in the summer of 2022. Knowledge of data-driven modeling techniques is required. Experience in working with thermo-fluid systems is preferred. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. Senior Ph.D. students in applied mathematics, chemical/mechanical engineering and other related areas are encouraged to apply. The expected duration of the internship is 3 months and the start date is flexible.

    • MS1838: Data-Driven Optimization for Building Energy Systems

      MERL is looking for a highly motivated and qualified candidate to work on data-driven, sample-efficient optimization with real-world applications in building energy systems. The ideal candidate will have a strong understanding machine learning or sampling-based optimization with expertise demonstrated via, e.g., publications, in at least one of: few-shot optimization, Bayesian methods, and/or learning for control/estimation of buildings and energy systems. Hands-on programming experience with standard ML toolkits such as PyTorch/Tensorflow is preferred; knowledge of additional, relevant tools (e.g., GPyTorch, Pyro) is a plus. PhD students are preferred, as an expected outcome of the internship is a publication in a high-tier venue. The minimum duration of the internship is 12 weeks; start time 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.

    • MS1851: Dynamic Modeling and Control for Grid-Interactive Buildings

      MERL is looking for a highly motivated and qualified candidate to work on modeling for smart sustainable buildings. The ideal candidate will have a strong understanding of modeling renewable energy sources, grid-interactive buildings, occupant behavior, and dynamical systems with expertise demonstrated via, e.g., peer-reviewed publications. Hands-on programming experience with Modelica is preferred. The minimum duration of the internship is 12 weeks; start time 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.


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  • Recent Publications

    •  Anantharaman, R., Abdelrahim, A., Martinuzzi, F., Yalburgi, S., Saba, E., Fischer, K., Hertz, G., de Vos, P., Laughman, C.R., Ma, Y., Shah, V., Edelman, A., Rackauckas, C., "Composable and Reusable Neural Surrogates to Predict System Response of Causal Model Components", AAAI 2022 Workshop on AI based Design and Manufacturing, March 2022.
      BibTeX TR2022-034 PDF
      • @inproceedings{Anantharaman2022mar,
      • author = {Anantharaman, Ranjan and Abdelrahim, Anas and Martinuzzi, Francesco and Yalburgi, Sharan and Saba, Elliot and Fischer, Keno and Hertz, Glen and de Vos, Pepijn and Laughman, Christopher R. and Ma, Yingbo and Shah, Viral and Edelman, Alan and Rackauckas, Chris},
      • title = {Composable and Reusable Neural Surrogates to Predict System Response of Causal Model Components},
      • booktitle = {AAAI 2022 Workshop on AI based Design and Manufacturing},
      • year = 2022,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2022-034}
      • }
    •  Bortoff, S.A., Schwerdtner, P., Danielson, C., Di Cairano, S., Burns, D.J., "H-Infinity Loop-Shaped Model Predictive Control with HVAC Application", IEEE Transactions on Control Systems Technology, March 2022.
      BibTeX TR2022-028 PDF
      • @article{Bortoff2022mar,
      • author = {Bortoff, Scott A. and Schwerdtner, Paul and Danielson, Claus and Di Cairano, Stefano and Burns, Daniel J.},
      • title = {H-Infinity Loop-Shaped Model Predictive Control with HVAC Application},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2022,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2022-028}
      • }
    •  Chakrabarty, A., Maddalena, E., Qiao, H., Laughman, C.R., "Scalable Bayesian Optimization for Model Calibration: Case Study on Coupled Building and HVAC Dynamics", Energy and Buildings, DOI: 10.1016/​j.enbuild.2021.111460, Vol. 253, pp. 111460, March 2022.
      BibTeX TR2022-030 PDF
      • @article{Chakrabarty2022mar,
      • author = {Chakrabarty, Ankush and Maddalena, Emilio and Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Scalable Bayesian Optimization for Model Calibration: Case Study on Coupled Building and HVAC Dynamics},
      • journal = {Energy and Buildings},
      • year = 2022,
      • volume = 253,
      • pages = 111460,
      • month = mar,
      • doi = {10.1016/j.enbuild.2021.111460},
      • url = {https://www.merl.com/publications/TR2022-030}
      • }
    •  Chakrabarty, A., Maddalena, E., Qiao, H., Laughman, C.R., "Scalable Bayesian Optimization for Parameter Estimation of Coupled Building and HVAC Dynamics", Energy and Buildings, DOI: 10.1016/​j.enbuild.2021.111460, Vol. 253, pp. 111460, March 2022.
      BibTeX TR2022-030 PDF
      • @article{Chakrabarty2022mar2,
      • author = {Chakrabarty, Ankush and Maddalena, Emilio and Qiao, Hongtao and Laughman, Christopher R.},
      • title = {Scalable Bayesian Optimization for Parameter Estimation of Coupled Building and HVAC Dynamics},
      • journal = {Energy and Buildings},
      • year = 2022,
      • volume = 253,
      • pages = 111460,
      • month = mar,
      • doi = {10.1016/j.enbuild.2021.111460},
      • url = {https://www.merl.com/publications/TR2022-030}
      • }
    •  Jeon, W., Chakrabarty, A., Zemouche, A., Rajamani, R., "Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications", IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/​TMECH.2021.3081035, Vol. 26, No. 4, pp. 1941-1950, January 2022.
      BibTeX TR2022-003 PDF
      • @article{Jeon2022jan,
      • author = {Jeon, Woongsun and Chakrabarty, Ankush and Zemouche, Ali and Rajamani, Rajesh},
      • title = {Simultaneous State Estimation and Tire Model Learning for Autonomous Vehicle Applications},
      • journal = {IEEE/ASME Transactions on Mechatronics},
      • year = 2022,
      • volume = 26,
      • number = 4,
      • pages = {1941--1950},
      • month = jan,
      • doi = {10.1109/TMECH.2021.3081035},
      • url = {https://www.merl.com/publications/TR2022-003}
      • }
    •  Teo, K.H., Zhang, Y., Chowdhury, N., Rakheja, S., Ma, R., Xie, Q., Yagyu, E., Yamanaka, K., Li, K., Palacios, T., "Emerging GaN technologies for power, RF, digital and quantum computing applications: recent advances and prospects", Journal of Applied Physics, DOI: 10.1063/​5.0061555, December 2021.
      BibTeX TR2022-002 PDF
      • @article{Teo2021dec,
      • author = {Teo, Koon Hoo and Zhang, Yuhao and Chowdhury, Nadim and Rakheja, Shaloo and Ma, Rui and Xie, Qingyun and Yagyu, Eiji and Yamanaka, Koji and Li, Kexin and Palacios, Tomas},
      • title = {Emerging GaN technologies for power, RF, digital and quantum computing applications: recent advances and prospects},
      • journal = {Journal of Applied Physics},
      • year = 2021,
      • month = dec,
      • doi = {10.1063/5.0061555},
      • url = {https://www.merl.com/publications/TR2022-002}
      • }
    •  Zhan, S., Wichern, G., Laughman, C.R., Chakrabarty, A., "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.
      BibTeX TR2021-149 PDF
      • @inproceedings{Zhan2021dec,
      • author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chakrabarty, Ankush},
      • title = {Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2021,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2021-149}
      • }
    •  Wang, B., Zhou, L., Miyoshi, M., Inoue, H., Kanemaru, M., "Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.23919/​ICEMS52562.2021.9634328, November 2021, pp. 944-948.
      BibTeX TR2021-139 PDF
      • @inproceedings{Wang2021nov3,
      • author = {Wang, Bingnan and Zhou, Lei and Miyoshi, Masahito and Inoue, hiroshi and Kanemaru, Makoto},
      • title = {Quantification of Induction Motor Bearing Fault Severity based on Modified Winding Function Theory},
      • booktitle = {2021 24th International Conference on Electrical Machines and Systems (ICEMS)},
      • year = 2021,
      • pages = {944--948},
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.23919/ICEMS52562.2021.9634328},
      • url = {https://www.merl.com/publications/TR2021-139}
      • }
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