Search Results for author:
Found 21 papers, 4 papers with code
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems.
Neural Differential Algebraic Equations
In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks.
A Robust, Efficient Predictive Safety Filter
In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation.
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Recent years have seen a rich literature of data-driven approaches designed for power grid applications.
Machine Learning for Smart and Energy-Efficient Buildings
In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions.
Structural Inference of Networked Dynamical Systems with Universal Differential Equations
Networked dynamical systems are common throughout science in engineering; e. g., biological networks, reaction networks, power systems, and the like.
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach
In recent years data-driven analysis of dynamical systems has attracted a lot of attention and transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being used almost ubiquitously.
Neuro-physical dynamic load modeling using differentiable parametric optimization
In this work, we investigate a data-driven approach for obtaining a reduced equivalent load model of distribution systems for electromechanical transient stability analysis.
Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem
The dynamics-aware economic dispatch (DED) problem embeds low-level generator dynamics and operational constraints to enable near real-time scheduling of generation units in a power network.
AutoNF: Automated Architecture Optimization of Normalizing Flows Using a Mixture Distribution Formulation
Although various flow models based on different transformations have been proposed, there still lacks a quantitative analysis of performance-cost trade-offs between different flows as well as a systematic way of constructing the best flow architecture.
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems.
Constrained Block Nonlinear Neural Dynamical Models
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics.
Dissipative Deep Neural Dynamical Systems
In this paper, we provide sufficient conditions for dissipativity and local asymptotic stability of discrete-time dynamical systems parametrized by deep neural networks.
Physics-Informed Neural State Space Models via Learning and Evolution
Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models.
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture.
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems
In the DPC framework, a neural state-space model is learned from time-series measurements of the system dynamics.
Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees
We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems with probabilistic performance guarantees.
Constrained Neural Ordinary Differential Equations with Stability Guarantees
Differential equations are frequently used in engineering domains, such as modeling and control of industrial systems, where safety and performance guarantees are of paramount importance.
