835 citations · 4k across the 32 of their papers we have counts for
30 papers · 1 filter
Studying oscillation death in two-dimensional cylinder-airfoil interactions with synchronization-theoretic autoencoder
Yuta Ushida, Daiki Beppu, Yuzuru Kato +1
Body-body interactions have long been investigated to leverage the performance of various fluid-based machines. This study analyzes the flow interaction between a cylinder and a NA…
Library-learning-assisted robust principal component analysis for denoising severely corrupted flow fields
Pablo Koop, Isabel Scherl, Kai Fukami
Large-amplitude entrywise corruption can distort flow-field data and contaminate extracted modes. Robust principal component analysis (RPCA) separates low-rank flow content from a…
Machine learning in fluid dynamics: A critical assessment
Kunihiko Taira, Georgios Rigas, Kai Fukami
The fluid dynamics community has increasingly adopted machine learning to analyze, model, predict, and control a wide range of flows. These methods offer powerful computational cap…
Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
Koji Fukagata, Kai Fukami
An autoencoder is a self-supervised machine-learning network trained to output a quantity identical to the input. Owing to its structure possessing a bottleneck with a lower dimens…
Single-snapshot machine learning for super-resolution of turbulence
Kai Fukami, Kunihiko Taira
Modern machine-learning techniques are generally considered data-hungry. However, this may not be the case for turbulence as each of its snapshots can hold more information than a…
Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions
Kai Fukami, Hiroya Nakao, Kunihiko Taira
We present a data-driven feedforward control to attenuate large transient lift experienced by an airfoil disturbed by an extreme level of discrete vortex gust. The current analysis…
Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables
Kai Fukami, Susumu Goto, Kunihiko Taira
Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate…
A cyclic perspective on transient gust encounters through the lens of persistent homology
Luke Smith, Kai Fukami, Girguis Sedky +2
Large amplitude gust encounters exhibit a range of separated flow phenomena, making them difficult to characterize using the traditional tools of aerodynamics. In this work, we pro…
Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning
Yonghong Zhong, Kai Fukami, Byungjin An +1
Reconstruction of unsteady vortical flow fields from limited sensor measurements is challenging. We develop machine learning methods to reconstruct flow features from sparse sensor…
Grasping Extreme Aerodynamics on a Low-Dimensional Manifold
Kai Fukami, Kunihiko Taira
Modern air vehicles perform a wide range of operations, including transportation, defense, surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations…
Image and video compression of fluid flow data
Vishal Anatharaman, Jason Feldkamp, Kai Fukami +1
We study the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The efficacy of spatial compression techniques, including JPEG…
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows
Kai Fukami, Koji Fukagata, Kunihiko Taira
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…
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
Masaki Morimoto, Kai Fukami, Romit Maulik +2
We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG ap…
Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows
Naoki Moriya, Kai Fukami, Yusuke Nabae +3
We propose a supervised-machine-learning-based wall model for coarse-grid wall-resolved large-eddy simulation (LES). Our consideration is made on LES of turbulent channel flows wit…
Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions
Taichi Nakamura, Kai Fukami, Koji Fukagata
Neural networks (NNs) and linear stochastic estimation (LSE) have widely been utilized as powerful tools for fluid-flow regressions. We investigate fundamental differences between…
Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks
Mitsuaki Matsuo, Kai Fukami, Taichi Nakamura +2
The recent development of high-performance computing enables us to generate spatio-temporal high-resolution data of nonlinear dynamical systems and to analyze them for a deeper und…
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
Masaki Morimoto, Kai Fukami, Kai Zhang +2
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…
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Kai Fukami, Romit Maulik, Nesar Ramachandra +2
Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction…
Probabilistic neural network-based reduced-order surrogate for fluid flows
Kai Fukami, Romit Maulik, Nesar Ramachandra +2
In recent years, there have been a surge in applications of neural networks (NNs) in physical sciences. Although various algorithmic advances have been proposed, there are, thus fa…
Generalization techniques of neural networks for fluid flow estimation
Masaki Morimoto, Kai Fukami, Kai Zhang +1
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…
Model order reduction with neural networks: Application to laminar and turbulent flows
Kai Fukami, Kazuto Hasegawa, Taichi Nakamura +2
We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of a convolutional…
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
Taichi Nakamura, Kai Fukami, Kazuto Hasegawa +2
We investigate the applicability of machine learning based reduced order model (ML-ROM) to three-dimensional complex flows. As an example, we consider a turbulent channel flow at t…
Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
Kai Fukami, Takaaki Murata, Kai Zhang +1
We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the threshold…
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
Romit Maulik, Kai Fukami, Nesar Ramachandra +2
We consider the use of probabilistic neural networks for fluid flow {surrogate modeling} and data recovery. This framework is constructed by assuming that the target variables are…