The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …
science and engineering, including experimental fluid dynamics, which is one of the original …
[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
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 …
User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems
Diffusion models are a class of probabilistic generative models that have been widely used
as a prior for image processing tasks like text conditional generation and inpainting. We …
as a prior for image processing tasks like text conditional generation and inpainting. We …
Unsteady cylinder wakes from arbitrary bodies with differentiable physics-assisted neural network
S Brahmachary, N Thuerey - Physical Review E, 2024 - APS
This work describes a hybrid predictive framework configured as a coarse-grained surrogate
for reconstructing unsteady fluid flows around multiple cylinders of diverse configurations …
for reconstructing unsteady fluid flows around multiple cylinders of diverse configurations …
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 …
Predicting turbulent dynamics with the convolutional autoencoder echo state network
The dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of
accurate reduced-order models challenging. The overarching objective of this paper is to …
accurate reduced-order models challenging. The overarching objective of this paper is to …
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 …
Data-driven prediction and control of extreme events in a chaotic flow
An extreme event is a sudden and violent change in the state of a nonlinear system. In fluid
dynamics, extreme events can have adverse effects on the system's optimal design and …
dynamics, extreme events can have adverse effects on the system's optimal design and …