Physics-informed neural network (PINN) evolution and beyond: A systematic literature review and bibliometric analysis
This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …
[HTML][HTML] A review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells
The real-time model-based control of polymer electrolyte membrane (PEM) fuel cells
requires a computationally efficient and sufficiently accurate model to predict the transient …
requires a computationally efficient and sufficiently accurate model to predict the transient …
Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study
In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …
traditional time-series forecasting approaches in many contexts, including univariate and …
Robust optimization and validation of echo state networks for learning chaotic dynamics
An approach to the time-accurate prediction of chaotic solutions is by learning temporal
patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing …
patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing …
Analogue and physical reservoir computing using water waves: Applications in power engineering and beyond
IS Maksymov - Energies, 2023 - mdpi.com
More than 3.5 billion people live in rural areas, where water and water energy resources
play an important role in ensuring sustainable and productive rural economies. This article …
play an important role in ensuring sustainable and productive rural economies. This article …
A review of advances towards efficient reduced-order models (ROM) for predicting urban airflow and pollutant dispersion
Computational fluid dynamics (CFD) models have been used for the simulation of urban
airflow and pollutant dispersion, due to their capability to capture different length scales and …
airflow and pollutant dispersion, due to their capability to capture different length scales and …
Short-and long-term predictions of chaotic flows and extreme events: a physics-constrained reservoir computing approach
We propose a physics-constrained machine learning method—based on reservoir
computing—to time-accurately predict extreme events and long-term velocity statistics in a …
computing—to time-accurately predict extreme events and long-term velocity statistics in a …
Stability analysis of chaotic systems from data
G Margazoglou, L Magri - Nonlinear Dynamics, 2023 - Springer
The prediction of the temporal dynamics of chaotic systems is challenging because
infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal …
infinitesimal perturbations grow exponentially. The analysis of the dynamics of infinitesimal …
A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks
Computational modeling and experimental/clinical prediction of the complex signals during
cardiac arrhythmias have the potential to lead to new approaches for prevention and …
cardiac arrhythmias have the potential to lead to new approaches for prevention and …
Constraining chaos: Enforcing dynamical invariants in the training of reservoir computers
Drawing on ergodic theory, we introduce a novel training method for machine learning
based forecasting methods for chaotic dynamical systems. The training enforces dynamical …
based forecasting methods for chaotic dynamical systems. The training enforces dynamical …