Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

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

We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …

In this paper, we introduce SciANN, a Python package for scientific computing and physics-
informed deep learning using artificial neural networks. SciANN uses the widely used deep …

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 …

High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …

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 …

We present a new data reconstruction method with supervised machine learning techniques
inspired by super resolution and inbetweening to recover high-resolution turbulent flows …

We investigate the applicability of the machine learning based reduced order model (ML-
ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel …

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 …