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Found 9 papers, 3 papers with code
Compositional Estimation of Lipschitz Constants for Deep Neural Networks
The Lipschitz constant plays a crucial role in certifying the robustness of neural networks to input perturbations and adversarial attacks, as well as the stability and safety of systems with neural network controllers.
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory.
Learning Dissipative Neural Dynamical Systems
The objective of this paper is to learn a neural dynamical model that approximates this system, while preserving the dissipativity property in the model.
Safe Control Design through Risk-Tunable Control Barrier Functions
We consider the problem of designing controllers to guarantee safety in a class of nonlinear systems under uncertainties in the system dynamics and/or the environment.
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems
One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov function for the system and characterize its region of attraction as the stability region.
The Impact of Heavy-Duty Vehicle Electrification on Large Power Grids: a Synthetic Texas Case Study
In fact, we find that as little as 11% of heavy duty vehicles in Texas charging simultaneously can lead to significant voltage violations on the transmission network that compromise grid reliability.
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change.
Safety during Transient Response in Direct Current Microgrids using Control Barrier Functions
To achieve this, we propose an optimization based controller design using control barrier functions.
Tracking and Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U. S. becoming the epicenter of COVID-19 cases and deaths in late March.
Computers and Society
