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
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News & Events
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EVENT MERL Virtual Open House 2020 Date & Time: Wednesday, December 9, 2020; 1:00-5:00PM EST
MERL Contacts: Elizabeth Phillips; Jeroen van Baar; Anthony Vetro
Location: Virtual
Research Areas: Applied Physics, Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Electric Systems, Electronic and Photonic Devices, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & AudioBrief- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
Registration: https://mailchi.mp/merl/merl-virtual-open-house-2020
Schedule: https://www.merl.com/events/voh20
Current internship and employment openings:
https://www.merl.com/internship/openings
https://www.merl.com/employment/employment
Information about working at MERL:
https://www.merl.com/employment
- MERL will host a virtual open house on December 9, 2020. Live sessions will be held from 1-5pm EST, including an overview of recent activities by our research groups and a talk by Prof. Pierre Moulin of University of Illinois at Urbana-Champaign on adversarial machine learning. Registered attendees will also be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities including internship, post-doc and research scientist openings, as well as visiting faculty positions.
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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, OptimizationBrief- 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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Internships
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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.
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MD1558: Symbolic regression
MERL is seeking a self-motivated intern to conduct fundamental research in the area of symbolic regression and deep learning for applications of recovering mathematical expressions or physical laws. The ideal candidate would be a senior PhD student with solid background in machine learning and strong publication record in top-tier venues. Prior experience in symbolic regression is strongly preferred. Very good Python, Pytorch/Tensorflow, and Matlab skills are required. The intern is expected to collaborate with MERL researchers to build models, develop algorithms, and prepare manuscripts for scientific publications. The expected duration of the internship is 3 months and the start date 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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MS1571: Data-based Dynamic Modeling of Vapor Compression Systems
MERL is seeking a motivated and qualified individual to conduct research in dynamic modeling of vapor compression systems. Knowledge of data-based modeling techniques such as neural network and support vector regression 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. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.
See All Internships for Multi-Physical Modeling -
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Recent Publications
- "Modelica-Based Control of A Delta Robot", ASME Dynamic Systems and Control Conference, December 2020.BibTeX TR2020-154 PDF
- @inproceedings{Bortoff2020dec,
- author = {Bortoff, Scott A. and Okasha, Ahmed},
- title = {Modelica-Based Control of A Delta Robot},
- booktitle = {ASME Dynamic Systems and Control Conference},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-154}
- }
, - "Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks", Advances in Neural Information Processing Systems (NeurIPS), December 2020.BibTeX TR2020-169 PDF
- @inproceedings{Anantharaman2020dec,
- author = {Anantharaman, Ranjan and Ma, Yingbo and Gowda, Shashi and Laughman, Christopher R. and Shah, Viral and Edelman, Alan and Rackauckas, Chris},
- title = {Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2020,
- month = dec,
- url = {https://www.merl.com/publications/TR2020-169}
- }
, - "End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping", European Conference on Optical Communication (ECOC), November 2020.BibTeX TR2020-155 PDF Video
- @inproceedings{Talreja2020nov,
- author = {Talreja, Veeru and Koike-Akino, Toshiaki and Wang, Ye and Millar, David S. and Kojima, Keisuke and Parsons, Kieran},
- title = {End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping},
- booktitle = {European Conference on Optical Communication (ECOC)},
- year = 2020,
- month = nov,
- url = {https://www.merl.com/publications/TR2020-155}
- }
, - "Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.23919/ICEMS50442.2020.9291153, November 2020, pp. 19-22.BibTeX TR2020-149 PDF
- @inproceedings{Shin2020nov,
- author = {Shin, Kyung-Hun and Wang, Bingnan},
- title = {Semi-Analytical Modeling for Interior Permanent Magnet Synchronous Machines Considering Permeability of Rotor Core},
- booktitle = {2020 23rd International Conference on Electrical Machines and Systems (ICEMS)},
- year = 2020,
- pages = {19--22},
- month = nov,
- doi = {10.23919/ICEMS50442.2020.9291153},
- url = {https://www.merl.com/publications/TR2020-149}
- }
, - "Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning", International Conference on Electrical Machines and Systems (ICEMS), DOI: 10.23919/ICEMS50442.2020.9291099, November 2020, pp. 1341-1346.BibTeX TR2020-151 PDF
- @inproceedings{Zhang2020nov2,
- author = {Zhang, Shen and Ye, Fei and Wang, Bingnan and Habetler, Thomas G},
- title = {Few-Shot Bearing Anomaly Detection via Model-Agnostic Meta-Learning},
- booktitle = {2020 23rd International Conference on Electrical Machines and Systems (ICEMS)},
- year = 2020,
- pages = {1341--1346},
- month = nov,
- doi = {10.23919/ICEMS50442.2020.9291099},
- url = {https://www.merl.com/publications/TR2020-151}
- }
, - "Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), November 2020.BibTeX TR2020-148 PDF
- @inproceedings{Wang2020nov2,
- author = {Wang, Bingnan and Hotta, Akira},
- title = {Contactless Eddy Current Sensing for Carbon Fiber Reinforced Polymer Defect Detection},
- booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
- year = 2020,
- month = nov,
- url = {https://www.merl.com/publications/TR2020-148}
- }
, - "Artificial Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems", IEEE Network, DOI: 10.1109/MNET.011.1900322, Vol. 34, No. 3, pp. 64-72, 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,
- volume = 34,
- number = 3,
- pages = {64--72},
- month = may,
- doi = {10.1109/MNET.011.1900322},
- url = {https://www.merl.com/publications/TR2020-058}
- }
, - "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}
- }
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- "Modelica-Based Control of A Delta Robot", ASME Dynamic Systems and Control Conference, December 2020.
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Videos