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.
Quick Links
-
Researchers
-
Awards
-
AWARD Best Paper Award at SDEMPED 2023 Date: August 30, 2023
Awarded to: Bingnan Wang, Hiroshi Inoue, and Makoto Kanemaru
MERL Contact: Bingnan Wang
Research Areas: Applied Physics, Data Analytics, Multi-Physical ModelingBrief- MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.
SDEMPED was established as the only international symposium entirely devoted to the diagnostics of electrical machines, power electronics and drives. It is now a regular biennial event. The 14th version, SDEMPED 2023 was held in Chania, Greece from August 28th to 31st, 2023.
- MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.
See All Awards for MERL -
-
News & Events
-
NEWS Ankush Chakrabarty gave a lecture at UT-Austin's Seminar Series on Occupant-Centric Grid-Interactive Buildings Date: March 20, 2024
Where: Austin, TX
MERL Contact: Ankush Chakrabarty
Research Areas: Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
The talk, entitled "Deep Generative Networks and Fine-Tuning for Net-Zero Energy Buildings" described lessons learned from MERL's recent research on generative models for building simulation and control, along with meta-learning for on-the-fly fine-tuning to adapt and optimize energy expenditure.
- Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems Team, was invited to speak as a guest lecturer in the seminar series on "Occupant-Centric Grid Interactive Buildings" in the Department of Civil, Architectural and Environmental Engineering (CAEE) at the University of Texas at Austin.
-
NEWS Ankush Chakrabarty served as Co-Chair of ACM BALANCES 2023 Date: November 14, 2023
Where: Istanbul, Turkey
MERL Contact: Ankush Chakrabarty
Research Areas: Control, Data Analytics, Machine Learning, Multi-Physical Modeling, OptimizationBrief- Ankush Chakrabarty, Principal Research Scientist in the Multiphysical Systems team at MERL, served as Co-Chair at the 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities (BALANCES'23). The workshop places spotlights on two different IEA EBC Annexes: the Annex 81 - Data-Driven Smart Buildings and Annex 82 - Energy Flexible Buildings Towards Resilient Low Carbon Energy Systems.
See All News & Events for Multi-Physical Modeling -
-
Internships
-
EA2050: Electric Motor Design and Electromagnetic Analysis
MERL is seeing a motivated and qualified individual to conduct research on electric motor design and modeling, with a strong focus on electromagnetic analysis. Ideal candidates should be Ph.D. students with solid background and publication record in one more research area on electric machines: electric and magnetic modeling, new machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Research experiences on modeling and analysis of electric machines and fault diagnosis are required. Hands-on experience with new motor design and data analysis techniques are highly desirable. Start date for this internship is flexible and the duration is 3-6 months.
See All Internships for Multi-Physical Modeling -
-
Recent Publications
- "Distributed Co-Design of Motors and Motions for Robotic Manipulators", European Control Conference (ECC), June 2024.BibTeX TR2024-083 PDF
- @inproceedings{Lu2024jun,
- author = {Lu, Zehui and Wang, Yebin and Sakamoto, Yusuke and Mou, Shaoshuai}},
- title = {Distributed Co-Design of Motors and Motions for Robotic Manipulators},
- booktitle = {European Control Conference (ECC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-083}
- }
, - "Sizing of Electric Vehicle Power Converter Based on Distributed Operating Points", 2024 IEEE Transportation Electrification Conference & Expo, June 2024.BibTeX TR2024-075 PDF
- @inproceedings{Rahman2024jun2,
- author = {Rahman, Syed and Wang, Yebin and Menner, Marcel and Liu, Dehong}},
- title = {Sizing of Electric Vehicle Power Converter Based on Distributed Operating Points},
- booktitle = {2024 IEEE Transportation Electrification Conference & Expo},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-075}
- }
, - "Permanent Magnet Motor Torque Waveform Prediction Using Learned Gap Flux", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), June 2024.BibTeX TR2024-065 PDF
- @inproceedings{Sakamoto2024jun,
- author = {Sakamoto, Yusuke and Wang, Bingnan and Yamamoto, Tatsuya and Nishimura, Yuki}},
- title = {Permanent Magnet Motor Torque Waveform Prediction Using Learned Gap Flux},
- booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-065}
- }
, - "Data-efficient Machine Learning Methods for Electric Motor Surrogate Models", Biennial IEEE Conference on Electromagnetic Field Computation (CEFC), June 2024.BibTeX TR2024-064 PDF
- @inproceedings{Wang2024jun2,
- author = {Wang, Bingnan and Sakamoto, Yusuke}},
- title = {Data-efficient Machine Learning Methods for Electric Motor Surrogate Models},
- booktitle = {Biennial IEEE Conference on Electromagnetic Field Computation (CEFC)},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-064}
- }
, - "Induction Motor Eccentricity Fault Detection and Quantification using Topological Data Analysis", IEEE Access, June 2024.BibTeX TR2024-063 PDF
- @article{Wang2024jun,
- author = {Wang, Bingnan and Lin, Chungwei and Inoue, Hiroshi and Kanemaru, Makoto}},
- title = {Induction Motor Eccentricity Fault Detection and Quantification using Topological Data Analysis},
- journal = {IEEE Access},
- year = 2024,
- month = jun,
- url = {https://www.merl.com/publications/TR2024-063}
- }
, - "Analytical Green’s functions for two-dimensional electrostatics and Boundary-element based solver", 2024 ACES (Applied Computational Electromagnetics Society) Symposium, May 2024.BibTeX TR2024-060 PDF
- @inproceedings{Lin2024may3,
- author = {Lin, Chungwei and Wang, Bingnan}},
- title = {Analytical Green’s functions for two-dimensional electrostatics and Boundary-element based solver},
- booktitle = {2024 ACES (Applied Computational Electromagnetics Society) Symposium},
- year = 2024,
- month = may,
- url = {https://www.merl.com/publications/TR2024-060}
- }
, - "Physically-constrained Hybrid Modeling for Vapor Compression Systems", Thermal and Fluids Engineering Conference, April 2024.BibTeX TR2024-038 PDF
- @inproceedings{Dong2024apr,
- author = {Dong, Yiyun and Qiao, Hongtao and Laughman, Christopher R.},
- title = {Physically-constrained Hybrid Modeling for Vapor Compression Systems},
- booktitle = {Thermal and Fluids Engineering Conference},
- year = 2024,
- month = apr,
- url = {https://www.merl.com/publications/TR2024-038}
- }
, - "Physics-informed shape optimization using coordinate projection", Scientific Reports, DOI: 10.1038/s41598-024-57137-4, Vol. 14, pp. 6537, April 2024.BibTeX TR2024-035 PDF
- @article{Zhang2024apr,
- author = {Zhang, Zhizhou and Lin, Chungwei and Wang, Bingnan},
- title = {Physics-informed shape optimization using coordinate projection},
- journal = {Scientific Reports},
- year = 2024,
- volume = 14,
- pages = 6537,
- month = apr,
- doi = {10.1038/s41598-024-57137-4},
- url = {https://www.merl.com/publications/TR2024-035}
- }
,
- "Distributed Co-Design of Motors and Motions for Robotic Manipulators", European Control Conference (ECC), June 2024.
-
Videos