Search Results for author: Jorge Cortés

Found 18 papers, 1 papers with code

Designing Poisson Integrators Through Machine Learning

no code implementations29 Mar 2024 Miguel Vaquero, David Martín de Diego, Jorge Cortés

This paper presents a general method to construct Poisson integrators, i. e., integrators that preserve the underlying Poisson geometry.

Unsupervised Learning for Equitable DER Control

no code implementations17 Mar 2024 Zhenyi Yuan, Guido Cavraro, Ahmed S. Zamzam, Jorge Cortés

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers.


Symmetry Preservation in Hamiltonian Systems: Simulation and Learning

no code implementations30 Aug 2023 Miguel Vaquero, Jorge Cortés, David Martín de Diego

This work presents a general geometric framework for simulating and learning the dynamics of Hamiltonian systems that are invariant under a Lie group of transformations.

Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers

no code implementations7 Jun 2023 Zhenyi Yuan, Guido Cavraro, Jorge Cortés

We consider the problem of learning local Volt/Var controllers in distribution grids (DGs).

Equilibria of Fully Decentralized Learning in Networked Systems

no code implementations15 May 2023 Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés

Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder their applicability to practical systems.

Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow

no code implementations20 Mar 2023 Jie Feng, Wenqi Cui, Jorge Cortés, Yuanyuan Shi

Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their deployment in real-world scenarios.

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

no code implementations26 Sep 2022 Zhenyi Yuan, Guido Cavraro, Manish K. Singh, Jorge Cortés

We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DN operating point agreeing with the local surrogates.

Temporal Forward-Backward Consistency, Not Residual Error, Measures the Prediction Accuracy of Extended Dynamic Mode Decomposition

no code implementations15 Jul 2022 Masih Haseli, Jorge Cortés

Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the action of the Koopman operator on a linear function space spanned by a dictionary of functions.

Dictionary Learning

Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach

no code implementations1 May 2022 Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés

Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch.

reinforcement-learning Reinforcement Learning (RL)

Selective Inhibition and Recruitment of Linear-Threshold Thalamocortical Networks

no code implementations3 Jan 2022 Michael McCreesh, Jorge Cortés

Neuroscientific evidence shows that for most brain networks all pathways between cortical regions either pass through the thalamus or a transthalamic parallel route exists for any direct corticocortical connection.

Generalizing Dynamic Mode Decomposition: Balancing Accuracy and Expressiveness in Koopman Approximations

no code implementations8 Aug 2021 Masih Haseli, Jorge Cortés

This paper tackles the data-driven approximation of unknown dynamical systems using Koopman-operator methods.

Learning Barrier Functions with Memory for Robust Safe Navigation

no code implementations3 Nov 2020 Kehan Long, Cheng Qian, Jorge Cortés, Nikolay Atanasov

Control barrier functions are widely used to enforce safety properties in robot motion planning and control.

Motion Planning Robotics

Parallel Learning of Koopman Eigenfunctions and Invariant Subspaces For Accurate Long-Term Prediction

no code implementations13 May 2020 Masih Haseli, Jorge Cortés

We identify conditions on the network topology to ensure the algorithm identifies the maximal Koopman-invariant subspace in the span of the original dictionary, characterize its time, computational, and communication complexity, and establish its robustness against communication failures.

Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs

1 code implementation15 Mar 2020 Francesca Boso, Dimitris Boskos, Jorge Cortés, Sonia Martínez, Daniel M. Tartakovsky

This study focuses on the latter step by investigating the spatio-temporal evolution of data-driven ambiguity sets and their associated guarantees when the random QoIs they describe obey hyperbolic partial-differential equations with random inputs.

Optimization and Control Analysis of PDEs

Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part I: Single-Layer Dynamics and Selective Inhibition

no code implementations5 Sep 2018 Erfan Nozari, Jorge Cortés

Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions.

Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part II: Multi-Layer Dynamics and Top-Down Recruitment

no code implementations5 Sep 2018 Erfan Nozari, Jorge Cortés

Goal-driven selective attention (GDSA) is a remarkable function that allows the complex dynamical networks of the brain to support coherent perception and cognition.

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