News & Events

6 News items, Awards, Events or Talks found.



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  •  NEWS    MERL researchers presenting workshop papers at NeurIPS 2022
    Date: December 2, 2022 - December 8, 2022
    MERL Contacts: Matthew Brand; Toshiaki Koike-Akino; Jing Liu; Saviz Mowlavi; Kieran Parsons; Ye Wang
    Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Signal Processing
    Brief
    • In addition to 5 papers in recent news (https://www.merl.com/news/news-20221129-1450), MERL researchers presented 2 papers at the NeurIPS Conference Workshop, which was held Dec. 2-8. NeurIPS is one of the most prestigious and competitive international conferences in machine learning.

      - “Optimal control of PDEs using physics-informed neural networks” by Saviz Mowlavi and Saleh Nabi

      Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural network-based surrogate model for the unknown state, PINNs can seamlessly blend measurement data with physical constraints. Here, we extend this framework to PDE-constrained optimal control problems, for which the governing PDE is fully known and the goal is to find a control variable that minimizes a desired cost objective. We validate the performance of the PINN framework by comparing it to state-of-the-art adjoint-based optimization, which performs gradient descent on the discretized control variable while satisfying the discretized PDE.

      - “Learning with noisy labels using low-dimensional model trajectory” by Vasu Singla, Shuchin Aeron, Toshiaki Koike-Akino, Matthew E. Brand, Kieran Parsons, Ye Wang

      Noisy annotations in real-world datasets pose a challenge for training deep neural networks (DNNs), detrimentally impacting generalization performance as incorrect labels may be memorized. In this work, we probe the observations that early stopping and low-dimensional subspace learning can help address this issue. First, we show that a prior method is sensitive to the early stopping hyper-parameter. Second, we investigate the effectiveness of PCA, for approximating the optimization trajectory under noisy label information. We propose to estimate the low-rank subspace through robust and structured variants of PCA, namely Robust PCA, and Sparse PCA. We find that the subspace estimated through these variants can be less sensitive to early stopping, and can outperform PCA to achieve better test error when trained on noisy labels.

      - In addition, new MERL researcher, Jing Liu, also presented a paper entitled “CoPur: Certifiably Robust Collaborative Inference via Feature Purification" based on his previous work before joining MERL. His paper was elected as a spotlight paper to be highlighted in lightening talks and featured paper panel.
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  •  NEWS    MERL Researcher Interviewed by Globest.com about "High Tech Airflow Control for Smarter Energy Use"
    Date: August 25, 2022
    MERL Contact: Anthony Vetro
    Research Areas: Dynamical Systems, Machine Learning, Multi-Physical Modeling
    Brief
    • MERL researcher Saleh Nabi was interviewed by Globest.com regarding the use of airflow optimization for smarter energy use and disease prevention. The article titled "High Tech Airflow Control for Smarter Energy Use: Reducing costs and improving effectiveness means a lot of tricky math" was recently published and describes how the solutions to complex fluid dynamical equations leads to improved HVAC control.

      Globest.com is a trusted and independent team of experts providing commercial real estate professionals with comprehensive coverage and best practices necessary to innovate and build their businesses. More details about Globest can be found here: https://www.globest.com/static/about-us/
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  •  NEWS    Saleh Nabi gave an invited talk at the Department of Mechanical Engineering at Rice University
    Date: September 30, 2020
    Where: Rice University
    Research Areas: Dynamical Systems, Optimization
    Brief
    • MERL researcher Dr. S. Nabi was invited to give a talk on the state-of-the-art methods for airflow optimization and control at Rice University. Several industrial applications to buoyancy-driven flows in the built environment, atmospheric flows, and prevention of transmission of COVID-19 were discussed. Furthermore, some novel advances on data-driven fluid mechanics for industrial applications were covered.
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  •  NEWS    MERL researchers presented 10 papers at American Control Conference (ACC)
    Date: July 1, 2020 - July 3, 2020
    Where: Denver, Colorado (virtual)
    MERL Contacts: Mouhacine Benosman; Karl Berntorp; Ankush Chakrabarty; Stefano Di Cairano; Yebin Wang; Avishai Weiss
    Research Areas: Control, Machine Learning, Optimization
    Brief
    • At the American Control Conference, MERL presented 10 papers on subjects including autonomous-vehicle decision making and motion planning, nonlinear estimation for thermal-fluid models and GNSS positioning, learning-based reference governors and reference governors for railway vehicles, and fail-safe rendezvous control.
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  •  NEWS    Turbulent flow paper selected as "Editors Suggestion" in journal Physical Review Fluids
    Date: January 11, 2019
    Where: PHYSICAL REVIEW FLUIDS, 4, 013801 – Published 11 January 2019
    Research Areas: Control, Dynamical Systems
    Brief
    • The journal Physical Review Fluids has recently instituted "...a service to our readers, we are formally marking a small number of papers published in Physical Review Fluids that the editors and referees find of particular interest, importance, or clarity." The following paper with MERL authors Saleh Nabi and Piyush Grover was so honored in the January 2019 issue: "Reduced-order modeling of fully turbulent buoyancy-driven flows using the Green's function method.".
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  •  NEWS    MERL researchers presented 11 papers at ACC 2017 (American Controls Conference)
    Date: May 24, 2017 - May 26, 2017
    MERL Contacts: Mouhacine Benosman; Stefano Di Cairano; Abraham Goldsmith; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang
    Research Areas: Control, Dynamical Systems, Machine Learning
    Brief
    • Talks were presented by members of several groups at MERL and covered a wide range of topics:
      - Similarity-Based Vehicle-Motion Prediction
      - Transfer Operator Based Approach for Optimal Stabilization of Stochastic Systems
      - Extended command governors for constraint enforcement in dual stage processing machines
      - Cooperative Optimal Output Regulation of Multi-Agent Systems Using Adaptive Dynamic Programming
      - Deep Reinforcement Learning for Partial Differential Equation Control
      - Indirect Adaptive MPC for Output Tracking of Uncertain Linear Polytopic Systems
      - Constraint Satisfaction for Switched Linear Systems with Restricted Dwell-Time
      - Path Planning and Integrated Collision Avoidance for Autonomous Vehicles
      - Least Squares Dynamics in Newton-Krylov Model Predictive Control
      - A Neuro-Adaptive Architecture for Extremum Seeking Control Using Hybrid Learning Dynamics
      - Robust POD Model Stabilization for the 3D Boussinesq Equations Based on Lyapunov Theory and Extremum Seeking.
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