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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  • Internships

    • 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.

    • MS1866: Deep Unsupervised/Semi-Supervised Learning for Smart Buildings

      MERL is seeking a highly motivated and qualified intern to collaborate with the Multiphysical Systems (MS) team in research on unsupervised/semi-supervised learning using data from real building energy systems. The ideal candidate is expected to be working towards a Ph.D. in deep learning for time-series, with special interest in learning representations for deep clustering. Fluency in Python and either PyTorch/Tensorflow is required. Previous peer-reviewed publications in related research topics and/or experience with mining from real-world data is preferred. The minimum duration of the internship is 12 weeks; start time is flexible.

    • MD1715: Electric Motor Fault Analysis

      MERL is seeing a motivated and qualified individual to conduct research on electric machine fault analysis and detection. The ideal candidate should have solid background in electric machine theory, modeling, numerical analysis, operation, and fault detection techniques, including machine learning. Research experiences on modeling and analysis of electric machines and fault detection are required. Hands-on experience with permanent magnet motor design and analysis, and knowledge on machine learning are desirable. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible.


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  • Recent Publications

    •  Zhan, S., Wichern, G., Laughman, C.R., Chong, A., Chakrabarty, A., "Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization", Energy and Buildings, DOI: 10.1016/​j.enbuild.2022.112278, Vol. 270, pp. 112278, September 2022.
      BibTeX TR2022-072 PDF
      • @article{Zhan2023jan,
      • author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chong, Adrian and Chakrabarty, Ankush},
      • title = {Calibrating building simulation models using multi-source datasets and meta-learned Bayesian optimization},
      • journal = {Energy and Buildings},
      • year = 2022,
      • volume = 270,
      • pages = 112278,
      • month = sep,
      • doi = {10.1016/j.enbuild.2022.112278},
      • url = {https://www.merl.com/publications/TR2022-072}
      • }
    •  de Castro, M., Wang, Y., Vanfretti, L., Wang, H., Liu, D., Bortoff, S.A., Takegami, T., "Modeling, Simulation and Control of Turboelectric Propulsion Systems for More Electric Aircrafts using Modelica", AIAA Aviation Forum, DOI: 10.2514/​6.2022-3873, June 2022, pp. 3873.
      BibTeX TR2022-087 PDF
      • @inproceedings{deCastro2022jun,
      • author = {de Castro, Marcelo and Wang, Yebin and Vanfretti, Luigi and Wang, Hongyu and Liu, Dehong and Bortoff, Scott A. and Takegami, Tomoki},
      • title = {Modeling, Simulation and Control of Turboelectric Propulsion Systems for More Electric Aircrafts using Modelica},
      • booktitle = {AIAA Aviation Forum},
      • year = 2022,
      • pages = 3873,
      • month = jun,
      • doi = {10.2514/6.2022-3873},
      • url = {https://www.merl.com/publications/TR2022-087}
      • }
    •  Deshpande, V., Laughman, C.R., Ma, Y., Rackauckas, C., "Constrained Smoothers for State Estimation of Vapor Compression Cycles", American Control Conference (ACC), June 2022.
      BibTeX TR2022-063 PDF
      • @inproceedings{Deshpande2022jun,
      • author = {Deshpande, Vedang and Laughman, Christopher R. and Ma, Yingbo and Rackauckas, Chris},
      • title = {Constrained Smoothers for State Estimation of Vapor Compression Cycles},
      • booktitle = {American Control Conference (ACC)},
      • year = 2022,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2022-063}
      • }
    •  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, DOI: 10.1109/​TCST.2022.3141937, Vol. 30, No. 5, pp. 2188-2203, 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,
      • volume = 30,
      • number = 5,
      • pages = {2188--2203},
      • month = mar,
      • doi = {10.1109/TCST.2022.3141937},
      • issn = {1063-6536},
      • url = {https://www.merl.com/publications/TR2022-028}
      • }
    •  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}
      • }
    •  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}
      • }
    •  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}
      • }
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