Dynamical Systems
Exploiting nonlinearity and shaping dynamics in creative and deeply mathematical ways.
We apply dynamical systems theory in applications ranging from space probe trajectory optimization to elevator suspensions. We also develop fundamental theory and computational methods in fluid dynamics.
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Researchers
Stefano
Di Cairano
Karl
Berntorp
Mouhacine
Benosman
Yebin
Wang
Avishai
Weiss
Scott A.
Bortoff
Christopher R.
Laughman
Hongtao
Qiao
Abraham P.
Vinod
Ankush
Chakrabarty
Hassan
Mansour
Saviz
Mowlavi
Daniel N.
Nikovski
Petros T.
Boufounos
Abraham
Goldsmith
Chungwei
Lin
Devesh K.
Jha
Pedro
Miraldo
Philip V.
Orlik
Diego
Romeres
Jianlin
Guo
Yanting
Ma
Kieran
Parsons
James
Queeney
Hongbo
Sun
Bingnan
Wang
Pu
(Perry)
WangWilliam S.
Yerazunis
Jinyun
Zhang
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Awards
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AWARD MERL’s Paper on Wi-Fi Sensing Earns Top 3% Paper Recognition at ICASSP 2023, Selected as a Best Student Paper Award Finalist Date: June 9, 2023
Awarded to: Cristian J. Vaca-Rubio, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Petros Boufounos and Petar Popovski
MERL Contacts: Petros T. Boufounos; Toshiaki Koike-Akino; Pu (Perry) Wang; Ye Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Dynamical Systems, Machine Learning, Signal ProcessingBrief- A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.
Performed during Cristian’s stay at MERL first as a visiting Marie Skłodowska-Curie Fellow and then as a full-time intern in 2022, this work capitalizes on standards-compliant Wi-Fi signals to perform indoor localization and sensing. The paper uses a neural dynamic learning framework to address technical issues such as low sampling rate and irregular sampling intervals.
ICASSP, a flagship conference of the IEEE Signal Processing Society (SPS), was hosted on the Greek island of Rhodes from June 04 to June 10, 2023. ICASSP 2023 marked the largest ICASSP in history, boasting over 4000 participants and 6128 submitted papers, out of which 2709 were accepted.
- A MERL Paper on Wi-Fi sensing was recognized as a Top 3% Paper among all 2709 accepted papers at the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Co-authored by Cristian Vaca-Rubio and Petar Popovski from Aalborg University, Denmark, and MERL researchers Pu Wang, Toshiaki Koike-Akino, Ye Wang, and Petros Boufounos, the paper "MmWave Wi-Fi Trajectory Estimation with Continous-Time Neural Dynamic Learning" was also a Best Student Paper Award finalist.
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News & Events
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NEWS Diego Romeres gave an invited talk at the Padua University's Seminar series on "AI in Action" Date: April 9, 2024
MERL Contact: Diego Romeres
Research Areas: Artificial Intelligence, Dynamical Systems, Machine Learning, Optimization, RoboticsBrief- Diego Romeres, Principal Research Scientist and Team Leader in the Optimization and Robotics Team, was invited to speak as a guest lecturer in the seminar series on "AI in Action" in the Department of Management and Engineering, at the University of Padua.
The talk, entitled "Machine Learning for Robotics and Automation" described MERL's recent research on machine learning and model-based reinforcement learning applied to robotics and automation.
- Diego Romeres, Principal Research Scientist and Team Leader in the Optimization and Robotics Team, was invited to speak as a guest lecturer in the seminar series on "AI in Action" in the Department of Management and Engineering, at the University of Padua.
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NEWS Saviz Mowlavi gave an invited talk at North Carolina State University Date: April 12, 2024
MERL Contact: Saviz Mowlavi
Research Areas: Control, Dynamical Systems, Machine Learning, OptimizationBrief- Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
The talk, entitled "Model-based and data-driven prediction and control of spatio-temporal systems", described the use of temporal smoothness to regularize the training of fast surrogate models for PDEs, user-friendly methods for PDE-constrained optimization, and efficient strategies for learning feedback controllers for PDEs.
- Saviz Mowlavi was invited to present remotely at the Computational and Applied Mathematics seminar series in the Department of Mathematics at North Carolina State University.
See All News & Events for Dynamical Systems -
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Internships
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CA1940: Autonomous vehicle planning and contro in uncertain environments
MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in research on planning and control for autonomous vehicles in uncertain surrounding environments. The research domain includes algorithms for path planning and control in environments that are uncertain and perceived by sensing and predicted according to models and data. The ideal candidate is expected to be working towards a PhD with strong emphasis in vehicle guidance and control, and to have interest and background in as many as possible of: vehicle dynamics modeling and control, sensor uncertainty modeling, data-driven prediction, predictive control for uncertain systems, motion planning. Good programming skills in MATLAB, Python are required, knowledge of C/C++, rapid prototyping systems, automatic code generation, vehicle simulation packages (CarSim, CarMaker) or ROS are a plus. The expected start of of the internship is in the late Spring/Early Summer 2022, for a duration of 3-6 months.
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ST2083: Deep Learning for Radar Perception
The Computation Sensing team at MERL is seeking a highly motivated intern to conduct fundamental research in radar perception. Expertise in deep learning-based object detection, multiple object tracking, data association, and representation learning (detection points, heatmaps, and raw radar waveforms) is required. Previous hands-on experience on open indoor/outdoor radar datasets is a plus. Familiarity with the concept of FMCW, MIMO, and range-Doppler-angle spectrum is an asset. The intern will collaborate with a small group of MERL researchers to develop novel algorithms, design experiments with MERL in-house testbed, and prepare results for patents and publication. The expected duration of the internship is 3 months with a flexible start date.
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CA2132: Optimization Algorithms for Motion Planning and Predictive Control
MERL is looking for a highly motivated and qualified individual to work on tailored computational algorithms for optimization-based motion planning and predictive control applications in autonomous systems (vehicles, mobile robots). The ideal candidate should have experience in either one or multiple of the following topics: convex and non-convex optimization, stochastic predictive control (e.g., scenario trees), interaction-aware motion planning, machine learning, learning-based model predictive control, mathematical programs with complementarity constraints (MPCCs), optimal control, and real-time optimization. PhD students in engineering or mathematics, especially with a focus on research related to any of the above topics are encouraged to apply. Publication of relevant results in conference proceedings or journals is expected. Capability of implementing the designs and algorithms in MATLAB/Python is required; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3 months, and the start date is flexible.
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Recent Publications
- "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", Transactions on Machine Learning Research (TMLR), April 2024.BibTeX TR2024-037 PDF
- @article{Queeney2024apr,
- author = {Queeney, James and Ozcan, Erhan Can and Paschalidis, Ioannis Ch. and Cassandras, Christos G.},
- title = {Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees},
- journal = {Transactions on Machine Learning Research (TMLR)},
- year = 2024,
- month = apr,
- issn = {2835-8856},
- url = {https://www.merl.com/publications/TR2024-037}
- }
, - "Lunar Landing with Feasible Divert using Controllable Sets", AIAA SciTech, DOI: 10.2514/6.2024-0324, January 2024, pp. AIAA 2024-0324.BibTeX TR2024-004 PDF
- @inproceedings{Srinivas2024jan,
- author = {Srinivas, Neeraj and Vinod, Abraham P. and Di Cairano, Stefano and Weiss, Avishai},
- title = {Lunar Landing with Feasible Divert using Controllable Sets},
- booktitle = {AIAA SCITECH 2024 Forum},
- year = 2024,
- pages = {AIAA 2024--0324},
- month = jan,
- doi = {10.2514/6.2024-0324},
- url = {https://www.merl.com/publications/TR2024-004}
- }
, - "Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments", Automatica, DOI: 10.1016/j.automatica.2023.111418, December 2023.BibTeX TR2023-147 PDF
- @article{Bonzanini2023dec,
- author = {Bonzanini, Angelo Domenico and Mesbah, Ali and Di Cairano, Stefano},
- title = {Perception-Aware Model Predictive Control for Constrained Control in Unknown Environments},
- journal = {Automatica},
- year = 2023,
- month = dec,
- doi = {10.1016/j.automatica.2023.111418},
- url = {https://www.merl.com/publications/TR2023-147}
- }
, - "Dual Parametric and State Estimation for Partial Differential Equations", IEEE Conference on Decision and Control, DOI: 10.1109/CDC49753.2023.10384246, December 2023, pp. 8156-8161.BibTeX TR2023-145 PDF
- @inproceedings{Mowlavi2023dec,
- author = {Mowlavi, Saviz and Benosman, Mouhacine},
- title = {Dual Parametric and State Estimation for Partial Differential Equations},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2023,
- pages = {8156--8161},
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/CDC49753.2023.10384246},
- issn = {2576-2370},
- isbn = {979-8-3503-0125-0},
- url = {https://www.merl.com/publications/TR2023-145}
- }
, - "Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning", Advances in Neural Information Processing Systems (NeurIPS), A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine, Eds., December 2023, pp. 1659-1680.BibTeX TR2023-143 PDF
- @inproceedings{Queeney2023dec,
- author = {Queeney, James and Benosman, Mouhacine},
- title = {Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2023,
- editor = {A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
- pages = {1659--1680},
- month = dec,
- publisher = {Curran Associates, Inc.},
- url = {https://www.merl.com/publications/TR2023-143}
- }
, - "A System-Level Cooperative Multi-Agent GNSS Positioning Solution", IEEE Transactions on Control Systems Technology, DOI: 10.1109/TCST.2023.3307339, Vol. 32, No. 1, pp. 158-173, October 2023.BibTeX TR2023-135 PDF
- @article{Greiff2023oct,
- author = {Greiff, Marcus and Di Cairano, Stefano and Kim, Kyeong Jin and Berntorp, Karl},
- title = {A System-Level Cooperative Multi-Agent GNSS Positioning Solution},
- journal = {IEEE Transactions on Control Systems Technology},
- year = 2023,
- volume = 32,
- number = 1,
- pages = {158--173},
- month = oct,
- doi = {10.1109/TCST.2023.3307339},
- url = {https://www.merl.com/publications/TR2023-135}
- }
, - "Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points", Nature Scientific Reports, DOI: 10.1038/s41598-023-36799-6, Vol. 13, No. 1, pp. 10166, October 2023.BibTeX TR2023-136 PDF
- @article{Sholokhov2023oct,
- author = {Sholokhov, Aleksei and Liu, Yuying and Mansour, Hassan and Nabi, Saleh},
- title = {Physics-Informed Neural ODE (PINODE): Embedding Physics into Models using Collocation Points},
- journal = {Nature Scientific Reports},
- year = 2023,
- volume = 13,
- number = 1,
- pages = 10166,
- month = oct,
- doi = {10.1038/s41598-023-36799-6},
- url = {https://www.merl.com/publications/TR2023-136}
- }
, - "High-Fidelity Simulation of Horizon-Based Optical Navigation with Open-Source Software", International Astronautical Congress (IAC), October 2023, pp. IAC-23,C1,5,9,x78805.BibTeX TR2023-128 PDF
- @inproceedings{Shimane2023oct,
- author = {Shimane, Yuri and Miraldo, Pedro and Berntorp, Karl and Greiff, Marcus and Elango, Purnanand and Weiss, Avishai},
- title = {High-Fidelity Simulation of Horizon-Based Optical Navigation with Open-Source Software},
- booktitle = {International Astronautical Congress (IAC)},
- year = 2023,
- pages = {IAC--23,C1,5,9,x78805},
- month = oct,
- url = {https://www.merl.com/publications/TR2023-128}
- }
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- "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", Transactions on Machine Learning Research (TMLR), April 2024.
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