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 …

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 …

We focus on a convolutional neural network (CNN), which has recently been utilized for fluid
flow analyses, from the perspective on the influence of various operations inside it by …

Parameter identification for wildfire forecasting models often relies on case-by-case tuning
or posterior diagnosis/analysis, which can be computationally expensive due to the …

Highly accurate simulations of complex phenomena governed by partial differential
equations (PDEs) typically require intrusive methods and entail expensive computational …

Closure problems are omnipresent when simulating multiscale systems, where some
quantities and processes cannot be fully prescribed despite their effects on the simulation's …

We propose a method using supervised machine learning to estimate velocity fields from
particle images having missing regions due to experimental limitations. As a first example, a …

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 …

We demonstrate several techniques to encourage practical uses of neural networks for fluid
flow estimation. In the present paper, three perspectives which are remaining challenges for …

Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …