Search Results for author:
Found 22 papers, 7 papers with code
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs).
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.
Neural Lyapunov Differentiable Predictive Control
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees.
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.
Learning Stochastic Parametric Differentiable Predictive Control Policies
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods.
Neural Ordinary Differential Equations for Nonlinear System Identification
In particular, we present a quantitative study comparing NODE's performance against neural state-space models and classical linear system identification methods.
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.
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images.
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.
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.
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.
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.
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data.
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique.
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.
Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection
Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases.
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection.
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
By treating system logs as threads of interleaved "sentences" (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior.
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams
As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time.
