227 citations · 524 across the 8 of their papers we have counts for
8 papers
Robust training approach of neural networks for fluid flow state estimations
Taichi Nakamura, Koji Fukagata
State estimation from limited sensor measurements is ubiquitously found as a common challenge in a broad range of fields including mechanics, astronomy, and geophysics. Fluid mecha…
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…
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…
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
Kai Fukami, Taichi Nakamura, Koji Fukagata
We propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields whil…
Quantum self-learning Monte Carlo with quantum Fourier transform sampler
Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii +1
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distrib…