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

    •  TALK   Universal Differential Equations for Scientific Machine Learning
      Date & Time: Thursday, May 7, 2020; 12:00 PM
      Speaker: Christopher Rackauckas, MIT
      MERL Host: Christopher Laughman
      Research Areas: Machine Learning, Multi-Physical Modeling, Optimization
      Brief
      • In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reco nciling data that is at odds with simplified models without requiring "big data". In this talk we discuss a new methodology, universal differential equations (UDEs), which augment scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.
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    •  NEWS   Scott Bortoff gave Mercer Distinguished Lecture at Rensselaer Polytechnic Institute
      Date: September 25, 2019
      Where: Rensselaer Polytechnic Institute (RPI), Troy, NY
      MERL Contact: Scott Bortoff
      Research Areas: Control, Multi-Physical Modeling
      Brief
      • The seminar, entitled “HVAC System Control and Optimization,” was part of the Mercer Distinguished Lecture Series in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI), Troy, NY. Given on Wednesday September 25, 2019, it focused on the systems engineering and control issues associated with highly integrated Heating, Ventilation and Air Conditioning Systems for low and zero energy buildings.
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  • Internships

    • MD1381: Electric Motor Design

      MERL is seeking a motivated and qualified individual to conduct research in design, modeling, and simulation of electrical machines. The ideal candidate should have solid backgrounds in modeling (including model reduction)/co-simulation of electromagnetics and thermal dynamics of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and real-time control experiments involving motor drives is a strong plus. Senior Ph.D. students in electrical or mechanical engineering are encouraged to apply. Start date for this internship is flexible and the duration is about 3-6 months.

    • MD1406: Numerical Analysis of Electric Machines

      MERL is seeking a motivated and qualified intern to conduct research in the design, modeling and optimization of electrical machines. The ideal candidate should have solid backgrounds in electromagnetic theory, electric machine design, and numerical modeling techniques (including model reduction), research experiences in electric, magnetic, and thermal modeling and analysis of electrical machines, and demonstrated capability to publish results in leading conferences/journals. Experience with ANSYS, COMSOL, and optimization techniques is a strong plus. Senior Ph.D. students in electrical or mechanical engineering with related expertise are encouraged to apply. Start date for this internship is flexible and the duration is 3-6 months.

    • MS1466: Modelica-Based Control of HVAC Equipment

      MERL seeks a highly motivated intern to develop an interface between real-time control systems that are implemented in the Modelica language, and laboratory HVAC equipment that is controlled by Labview. The control algorithms are developed using our Modelica library of HVAC components, and are realized natively in the Modelica language using the Synchronous Library. They are run in real-time on a PC using the Modelica Device Drivers library, and communicate with the Labview system via UDP. The intern would be responsible for developing professional-grade code to mature this interface, and then conduct experiments to test new control algorithms in our laboratory. Expertise using software development tools, such as Microsoft Visual Studio and network protocols such as UDP, is necessary. Experience with Modelica is strongly preferred. Knowledge and experience of vapor compression systems is also strongly preferred. Knowledge of control theory, including classical feedback and finite state machines, along with related laboratory experience is required. On-site employment is preferred, although it may be possible to conduct this work remotely. Students enrolled in a Masters or Ph.D. degree program of study are encouraged to apply. The internship is expected to be 3-6 months in duration, preferably in the fall or winter, 2020.


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  • Recent Publications

    •  Bhamidipati, S., Kim, K.J., Sun, H., Orlik, P.V., "Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems", IEEE Network, May 2020.
      BibTeX TR2020-058 PDF
      • @article{Bhamidipati2020may,
      • author = {Bhamidipati, Sriramya and Kim, Kyeong Jin and Sun, Hongbo and Orlik, Philip V.},
      • title = {Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems},
      • journal = {IEEE Network},
      • year = 2020,
      • month = may,
      • url = {https://www.merl.com/publications/TR2020-058}
      • }
    •  Zhang, S., Wang, B., Kanemaru, M., Lin, C., Liu, D., Habetler, T., "Model-Based Analysis and Quantification of Bearing Faults in Induction Machines", IEEE Transactions on Industry Applications, DOI: 10.1109/TIA.2020.2979383, Vol. 56, No. 3, pp. 2158-2170, May 2020.
      BibTeX TR2020-059 PDF
      • @article{Zhang2020may,
      • author = {Zhang, Shen and Wang, Bingnan and Kanemaru, Makoto and Lin, Chungwei and Liu, Dehong and Habetler, Thomas},
      • title = {Model-Based Analysis and Quantification of Bearing Faults in Induction Machines},
      • journal = {IEEE Transactions on Industry Applications},
      • year = 2020,
      • volume = 56,
      • number = 3,
      • pages = {2158--2170},
      • month = may,
      • doi = {10.1109/TIA.2020.2979383},
      • issn = {1939-9367},
      • url = {https://www.merl.com/publications/TR2020-059}
      • }
    •  Zhang, S., Zhang, S., Wang, B., Habetler, T., "Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review", IEEE Access, DOI: 10.1109/ACCESS.2020.2972859, Vol. 8, pp. 29857-29881, March 2020.
      BibTeX TR2020-034 PDF
      • @article{Zhang2020mar,
      • author = {Zhang, Shen and Zhang, Shibo and Wang, Bingnan and Habetler, Thomas},
      • title = {Deep Learning Algorithms for Bearing Fault Diagnostics – A Comprehensive Review},
      • journal = {IEEE Access},
      • year = 2020,
      • volume = 8,
      • pages = {29857--29881},
      • month = mar,
      • doi = {10.1109/ACCESS.2020.2972859},
      • issn = {2169-3536},
      • url = {https://www.merl.com/publications/TR2020-034}
      • }
    •  Bortoff, S.A., "Modeling Contact and Collisions for Robotic Assembly Control", American Modelica Conference 2020, March 2020.
      BibTeX TR2020-032 PDF
      • @inproceedings{Bortoff2020mar,
      • author = {Bortoff, Scott A.},
      • title = {Modeling Contact and Collisions for Robotic Assembly Control},
      • booktitle = {American Modelica Conference 2020},
      • year = 2020,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2020-032}
      • }
    •  Laughman, C.R., Bortoff, S.A., "Nonlinear State Estimation with FMI: Tutorial and Applications", American Modelica Conference 2020, March 2020.
      BibTeX TR2020-031 PDF Software
      • @inproceedings{Laughman2020mar,
      • author = {Laughman, Christopher R. and Bortoff, Scott A.},
      • title = {Nonlinear State Estimation with FMI: Tutorial and Applications},
      • booktitle = {American Modelica Conference 2020},
      • year = 2020,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2020-031}
      • }
    •  Lin, C., Sels, D., Wang, Y., "Time-optimal Control of a Dissipative Qubit", Physical Review, DOI: 10.1103/PhysRevA.101.022320, Vol. 101, No. 2, pp. 022320, February 2020.
      BibTeX TR2020-023 PDF
      • @article{Lin2020feb,
      • author = {Lin, Chungwei and Sels, Dries and Wang, Yebin},
      • title = {Time-optimal Control of a Dissipative Qubit},
      • journal = {Physical Review},
      • year = 2020,
      • volume = 101,
      • number = 2,
      • pages = 022320,
      • month = feb,
      • doi = {10.1103/PhysRevA.101.022320},
      • url = {https://www.merl.com/publications/TR2020-023}
      • }
    •  Teo, K.H., "Report on ISPSD 2019," Tech. Rep. TR2019-161, Mitsubishi Electric Research Laboratories, December 2019.
      BibTeX TR2019-161 PDF
      • @techreport{Teo2019dec,
      • author = {Teo, Koo Hoo},
      • title = {Report on ISPSD 2019},
      • institution = {Mitsubishi Electric Research Laboratories},
      • year = 2019,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2019-161}
      • }
    •  Garcia, J., Danielson, C., Limon, D., Bortoff, S.A., Di Cairano, S., "Steady-State Analysis of HVAC Performance using Indoor Fans in Control Design", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029730, December 2019, pp. 2952-2957.
      BibTeX TR2019-143 PDF
      • @inproceedings{Garcia2019dec,
      • author = {Garcia, Joaquin and Danielson, Claus and Limon, Daniel and Bortoff, Scott A. and Di Cairano, Stefano},
      • title = {Steady-State Analysis of HVAC Performance using Indoor Fans in Control Design},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2019,
      • pages = {2952--2957},
      • month = dec,
      • doi = {10.1109/CDC40024.2019.9029730},
      • url = {https://www.merl.com/publications/TR2019-143}
      • }
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