Search Results for author: Kai Fukami

Found 8 papers, 4 papers with code

Phase autoencoder for limit-cycle oscillators

no code implementations28 Feb 2024 Koichiro Yawata, Kai Fukami, Kunihiko Taira, Hiroya Nakao

We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics.

Time Series

Grasping Extreme Aerodynamics on a Low-Dimensional Manifold

1 code implementation13 May 2023 Kai Fukami, Kunihiko Taira

We reveal that the nonlinear vortical flow field over time and parameter space can be compressed to only three variables with a lift-augmented autoencoder while holding the essence of the original high-dimensional physics.

Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

no code implementations16 Sep 2021 Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata

The average of such an ensemble can be regarded as the `mean estimation', whereas its standard deviation can be used to construct `confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks.

regression Uncertainty Quantification

Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization

no code implementations7 Jan 2021 Masaki Morimoto, Kai Fukami, Kai Zhang, Aditya G. Nair, Koji Fukagata

We then discuss the influence of various parameters and operations on the CNN performance, with the utilization of autoencoder (AE).

Dimensionality Reduction Fluid Dynamics Computational Physics

Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning

1 code implementation3 Jan 2021 Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira

This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems.

Super-Resolution

Probabilistic neural networks for fluid flow surrogate modeling and data recovery

1 code implementation8 May 2020 Romit Maulik, Kai Fukami, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira

We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery.

Fluid Dynamics

Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes

1 code implementation17 Mar 2020 Kazuto Hasegawa, Kai Fukami, Takaaki Murata, Koji Fukagata

The present ML-ROMs are trained on a set of 80 bluff body shapes and tested on a different set of 20 bluff body shapes not used for training, with both training and test shapes chosen from the same random distribution.

Fluid Dynamics Computational Physics

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