Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
[HTML][HTML] Training stiff neural ordinary differential equations in data-driven wastewater process modelling
Neural ordinary differential equations (NODEs) have emerged as a powerful tool for data-
driven modelling of dynamical systems (R. Chen et al., 2018). Unlike traditional machine …
driven modelling of dynamical systems (R. Chen et al., 2018). Unlike traditional machine …
Learning Power System Dynamics with Noisy Data Using Neural Ordinary Differential Equations
The ability to learn complex system dynamics is crucial to enhancing the reliability and
stability of power systems. In this paper, we develop a novel neural ordinary differential …
stability of power systems. In this paper, we develop a novel neural ordinary differential …
Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) …
With the global energy transition of the electric power system, grid control, supervision, and
protection is becoming more challenging. With the increasing integration of renewable …
protection is becoming more challenging. With the increasing integration of renewable …
Learning power system dynamics with nearly-Hamiltonian neural network
The ability to learn power system dynamic model and predict transient trajectories using
data is crucial to realizing closed-loop control of the system with artificial intelligence. This …
data is crucial to realizing closed-loop control of the system with artificial intelligence. This …
Application of Neural Ordinary Differential Equations to Power System Frequency Dynamics
Electric power system control, supervision, and protection actions require an accurate
system dynamics model. The swing equation-based modeling approach does not properly …
system dynamics model. The swing equation-based modeling approach does not properly …
Financial Time Series Prediction via Neural Ordinary Differential Equations Approach
This paper considers the prediction problem of financial time series, namely, the prediction
of exchange rate for four currencies with the Chinese Yuan (CNY). A novel approach is …
of exchange rate for four currencies with the Chinese Yuan (CNY). A novel approach is …
Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks
In recent years, scientific machine learning, particularly physic-informed neural networks
(PINNs), has introduced new innovative methods to understanding the differential equations …
(PINNs), has introduced new innovative methods to understanding the differential equations …
Parameter Estimation of Synchronous Generator Using Neural Controlled Differential Equations
This paper introduces a synchronous generator modeling method based on neural
controlled differential equations (neural CDEs) using online sampled data. This method …
controlled differential equations (neural CDEs) using online sampled data. This method …
Parameter identification through gradient flow on latent variables
M Boulakia, H Liu, D Lombardi - 2023 - inria.hal.science
In this article, we consider a system of parametric ODEs which involves unknown
parameters and we seek to identify the values of the parameters associated to a given …
parameters and we seek to identify the values of the parameters associated to a given …
[PDF][PDF] Physics-informed ML for power grids
VJ Nair - 2023 - raw.githubusercontent.com
In this project, I explore the use of physics-informed machine learning for simulating and
analyzing transmission grids, to aid planning as well as real-time operations. Scientific …
analyzing transmission grids, to aid planning as well as real-time operations. Scientific …