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

  • News & Events

    •  NEWS    MERL researchers win ASME Energy Systems Technical Committee Best Paper Award at 2022 American Control Conference
      Date: June 8, 2022
      Where: 2022 American Control Conference
      MERL Contacts: Ankush Chakrabarty; Christopher R. Laughman
      Research Areas: Control, Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • Researchers from EPFL (Wenjie Xu, Colin Jones) and EMPA (Bratislav Svetozarevic), in collaboration with MERL researchers Ankush Chakrabarty and Chris Laughman, recently won the ASME Energy Systems Technical Committee Best Paper Award at the 2022 American Control Conference for their work on "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Performance Optimization with Unmodeled Constraints" out of 19 nominations and 3 finalists. The paper describes a data-driven framework for optimizing the performance of constrained control systems by systematically re-evaluating how cautiously/aggressively one should explore the search space to avoid sustained, large-magnitude constraint violations while tolerating small violations, and demonstrates these methods on a physics-based model of a vapor compression cycle.
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    •  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, BenoƮt 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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  • Internships

    • 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.

    • MD1714: Electric Motor Design

      MERL is seeing a motivated and qualified individual to conduct research on electric machine design, prototype, and experiment tests. The ideal candidate should have solid background and demonstrated research experience in electric machine theory, design analysis, motor drives, and control. Hands-on experiences on electric motor design and prototyping, test bench set up, and experiment measurements are required. Senior Ph.D. students in electrical engineering or mechanical engineering with 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, 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 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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