Dynamic mode decomposition (DMD) is a factorization and dimensionality reduction
technique for data sequences. In its most common form, it processes high-dimensional …

The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …

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 …

This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

This paper provides a short overview of how to use machine learning to build data-driven
models in fluid mechanics. The process of machine learning is broken down into five …

We investigate the capability of neural network-based model order reduction, ie,
autoencoder (AE), for fluid flows. As an example model, an AE which comprises of …

The structural response field is crucial for understanding mechanical behavior, especially
under uncertain conditions. However, current uncertainty quantification predominantly …

Research in modern data-driven dynamical systems is typically focused on the three key
challenges of high dimensionality, unknown dynamics and nonlinearity. The dynamic mode …

Dynamic mode decomposition (DMD) provides a regression framework for adaptively
learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal …