Applied Physics
From first-principles modeling to device designs.
Our research in this area uses physics to develop new technologies or solve an engineering problem, including optimal design of freeform optics, metamaterials, photonic and solid-state semiconductor devices; the modeling and analysis of electro-magnetic systems and studies on superconductivity and magnets.
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Researchers
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Awards
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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.
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News & Events
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TALK [MERL Seminar Series 2023] Prof. Faruque Hasan presents talk titled A Process Systems Engineering Perspective on Carbon Capture: Key Challenges and Opportunities Date & Time: Tuesday, September 19, 2023; 1:00 PM
Speaker: Faruque Hasan, Texas A&M University
MERL Host: Scott A. Bortoff
Research Areas: Applied Physics, Machine Learning, Multi-Physical Modeling, OptimizationAbstract
Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize fossil-based power and industrial sectors and is a bridging technology for a sustainable transition to a net-zero emission energy future. This talk aims to provide an overview of design and optimization of CCUS systems. I will also attempt to give a brief perspective on emerging interests in process systems engineering research (e.g., systems integration, multiscale modeling, strategic planning, and optimization under uncertainty). The purpose is not to cover all aspects of PSE research for CCUS but rather to foster discussion by presenting some plausible future directions and ideas.
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NEWS Ankush Chakrabarty co-organized three sessions at the ACC2023, and was nominated for Best Energy Systems Paper. Date: June 30, 2023 - June 2, 2023
Where: San Diego, CA
MERL Contact: Ankush Chakrabarty
Research Areas: Applied Physics, Artificial Intelligence, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- Ankush Chakrabarty (researcher, Multiphysical Systems Team) co-organized and spoke at 3 sessions at the 2023 American Control Conference in San Diego, CA. These include: (1) A tutorial session (w/ Stefano Di Cairano) on "Physics Informed Machine Learning for Modeling and Control": an effort with contributions from multiple academic institutes and US research labs; (2) An invited session on "Energy Efficiency in Smart Buildings and Cities" in which his paper (w/ Chris Laughman) on "Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems" was nominated for Best Energy Systems Paper Award; and, (3) A special session on Diversity, Equity, and Inclusion to improve recruitment and retention of underrepresented groups in STEM research.
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Internships
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MS0102: Internship - Estimation and Calibration of Multi-physical Systems Using Experiments
MERL is looking for a highly motivated and qualified candidate to work on estimation and calibration of muti-physical systems governed by differential algebraic equations (DAEs). The research will involve study, development and efficient implementation of estimation/calibration approaches for large-scale nonlinear systems, e.g., vapor compression cycles, with limited experimental data. The ideal candidate will have a strong background in one or multiple of the following topics: nonlinear control and estimation, optimization, and model calibration; with expertise demonstrated via, e.g., peer-reviewed publications. Prior experience in working with experimental data, and programming in Julia/Modelica is a plus. Senior PhD students in mechanical, electrical, chemical engineering or related fields are encouraged to apply. The typical duration of internship is 3 months, and the start date is flexible.
Required Specific Experience
- Graduate student with 2+ years of relevant research experience
Additional Desired Experience
- Strong programming skills in Julia or Modelica
- Prior experience in working with thermofluid systems
- Prior experience in estimation/calibration of complex nonlinear systems using experimental data
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CI0082: Internship - Quantum AI
MERL is excited to announce an internship opportunity in the field of Quantum Machine Learning (QML) and Quantum AI (QAI). We are seeking a highly motivated and talented individual to join our research team. This is an exciting opportunity to make a real impact in the field of quantum computing and AI, with the aim of publishing at leading research venues.
Responsibilities:
- Conduct cutting-edge research in quantum machine learning.
- Collaborate with a team of experts in quantum computing, deep learning, and signal processing.
- Develop and implement algorithms using PyTorch and PennyLane.
- Publish research results at leading research venues.
Qualifications:
- Currently pursuing a PhD or a post-graduate researcher in a relevant field.
- Strong background and solid publication records in quantum computing, deep learning, and signal processing.
- Proficient programming skills in PyTorch and PennyLane are highly desirable.
What We Offer:
- An opportunity to work on groundbreaking research in a leading research lab.
- Collaboration with a team of experienced researchers.
- A stimulating and supportive work environment.
If you are passionate about quantum machine learning and meet the above qualifications, we encourage you to apply. Please submit your resume and a brief cover letter detailing your research experience and interests. Join us at MERL and contribute to the future of quantum machine learning!
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EA0072: Internship - Electric Machine Topology Optimization
MERL is seeking a motivated and qualified intern to conduct research on shape and topology optimization of electrical machines. The ideal candidate should have a solid background and demonstrated research experience in mathematical optimization methods, including topology optimization, robust optimization, and sensitivity analysis, as well as machine learning methods. Hands-on coding experience with the implementation of topology optimization algorithms and finite-element simulation are desirable. Knowledge and experience with electric machine principle, design and finite-element analysis is a strong plus. Senior Ph.D. students in related expertise are encouraged to apply. Start date for this internship is flexible and the duration is around 3 months.
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Recent Publications
- , "System-Technology Co-Optimization of Multimetal Gated AlGaN/GaN HEMT for Improved RF Linearity", Journal of Electron Devices Society, December 2024.BibTeX TR2024-171 PDF
- @article{Hossain2024dec,
- author = {Hossain, Toiyob and Hossain, Tanvir and Anindya Alam, A. and sikder, Bejoy and Xie, Qingyun and Yuan, Mengyang and Yagyu, Eiji and Teo, Koon Hoo and Palacios, Tomas and Chowdhury, Nadim and Wang, Yebin}},
- title = {System-Technology Co-Optimization of Multimetal Gated AlGaN/GaN HEMT for Improved RF Linearity},
- journal = {Journal of Electron Devices Society},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-171}
- }
- , "Path Generation based on Electrostatic Equipotential Curves", IEEE Access, DOI: 10.1109/ACCESS.2024.3389962, Vol. 12, pp. 55019-55032, May 2024.BibTeX TR2024-049 PDF
- @article{Lin2024may,
- author = {Lin, Chungwei and Wang, Yebin and Vetterling, William and Jha, Devesh K. and Quirynen, Rien}},
- title = {Path Generation based on Electrostatic Equipotential Curves},
- journal = {IEEE Access},
- year = 2024,
- volume = 12,
- pages = {55019--55032},
- month = may,
- doi = {10.1109/ACCESS.2024.3389962},
- url = {https://www.merl.com/publications/TR2024-049}
- }
- , "Electric Machine Inverse Design with Variational Auto-Encoder (VAE)", IEEE Energy Conversion Congress and Exposition (ECCE), DOI: 10.1109/ECCE53617.2023.10362903, October 2023.BibTeX TR2023-134 PDF
- @inproceedings{Xu2023nov,
- author = {Xu, Yihao and Wang, Bingnan and Sakamoto, Yusuke and Yamamoto, Tatsuya and Nishimura, Yuki and Koike-Akino, Toshiaki and Wang, Ye},
- title = {Electric Machine Inverse Design with Variational Auto-Encoder (VAE)},
- booktitle = {IEEE Energy Conversion Congress and Exposition (ECCE)},
- year = 2023,
- month = oct,
- publisher = {IEEE},
- doi = {10.1109/ECCE53617.2023.10362903},
- issn = {2329-3748},
- isbn = {979-8-3503-1644-5},
- url = {https://www.merl.com/publications/TR2023-134}
- }
- , "Scene depths from a two-polarization metalens", Optica Imaging Congress / Flat Optics, DOI: 10.1364/FLATOPTICS.2023.FW5D.3, August 2023.BibTeX TR2023-105 PDF
- @inproceedings{Brand2023aug,
- author = {Brand, Matthew and Kuang, Zeyu},
- title = {Scene depths from a two-polarization metalens},
- booktitle = {Optica Imaging Congress / Flat Optics},
- year = 2023,
- month = aug,
- doi = {10.1364/FLATOPTICS.2023.FW5D.3},
- isbn = {978-1-957171-28-9},
- url = {https://www.merl.com/publications/TR2023-105}
- }
- , "Comparison of Learning-based Surrogate Models for Electric Motors", Conference on the Computation of Electromagnetic Fields (COMPUMAG), DOI: 10.1109/COMPUMAG56388.2023.10411811, May 2023, pp. 1-4.BibTeX TR2023-042 PDF
- @inproceedings{Xu2023may,
- author = {Xu, Yihao and Wang, Bingnan and Sakamoto, Yusuke and Yamamoto, Tatsuya and Nishimura, Yuki},
- title = {Comparison of Learning-based Surrogate Models for Electric Motors},
- booktitle = {2023 24th International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
- year = 2023,
- pages = {1--4},
- month = may,
- publisher = {IEEE},
- doi = {10.1109/COMPUMAG56388.2023.10411811},
- url = {https://www.merl.com/publications/TR2023-042}
- }
- , "Multi-Objective Motor Design Optimization with Physics-Assisted Neural Network Model", IEEE International Electric Machines and Drives Conference (IEMDC), DOI: 10.1109/IEMDC55163.2023.10238886, May 2023, pp. 1-7.BibTeX TR2023-038 PDF
- @inproceedings{Sakamoto2023may,
- author = {Sakamoto, Yusuke and Xu, Yihao and Wang, Bingnan and Yamamoto, Tatsuya and Nishimura, Yuki},
- title = {Multi-Objective Motor Design Optimization with Physics-Assisted Neural Network Model},
- booktitle = {2023 IEEE International Electric Machines & Drives Conference (IEMDC)},
- year = 2023,
- pages = {1--7},
- month = may,
- publisher = {IEEE},
- doi = {10.1109/IEMDC55163.2023.10238886},
- url = {https://www.merl.com/publications/TR2023-038}
- }
- , "Tandem Neural Networks for Electric Machine Inverse Design", IEEE International Electric Machines and Drives Conference (IEMDC), DOI: 10.1109/IEMDC55163.2023.10238921, May 2023, pp. 1-7.BibTeX TR2023-040 PDF
- @inproceedings{Xu2023may2,
- author = {Xu, Yihao and Wang, Bingnan and Sakamoto, Yusuke and Yamamoto, Tatsuya and Nishimura, Yuki and Koike-Akino, Toshiaki and Wang, Ye},
- title = {Tandem Neural Networks for Electric Machine Inverse Design},
- booktitle = {2023 IEEE International Electric Machines & Drives Conference (IEMDC)},
- year = 2023,
- pages = {1--7},
- month = may,
- publisher = {IEEE},
- doi = {10.1109/IEMDC55163.2023.10238921},
- url = {https://www.merl.com/publications/TR2023-040}
- }
- , "Deep Born Operator Learning for Reflection Tomographic Imaging", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP49357.2023.10095494, May 2023, pp. 1-5.BibTeX TR2023-029 PDF Video
- @inproceedings{Zhao2023may,
- author = {Zhao, Qingqing and Ma, Yanting and Boufounos, Petros T. and Nabi, Saleh and Mansour, Hassan},
- title = {Deep Born Operator Learning for Reflection Tomographic Imaging},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2023,
- pages = {1--5},
- month = may,
- publisher = {IEEE},
- doi = {10.1109/ICASSP49357.2023.10095494},
- url = {https://www.merl.com/publications/TR2023-029}
- }
- , "System-Technology Co-Optimization of Multimetal Gated AlGaN/GaN HEMT for Improved RF Linearity", Journal of Electron Devices Society, December 2024.
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Videos
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