Data-driven closure model for large-scale eddies in the energy-containing range of turbulence
Published in , 2025
Recommended citation: S. Matsumoto, M. Inubushi & S. Goto, Phys. Rev. Fluids (under review).
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Temporal coarse-graining of training data for data-driven closure modeling
Published in , 2025
Recommended citation: S. Matsumoto, M. Inubushi & S. Goto, submitted to Int. J. Heat Fluid Flow.
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Stable reproducibility of turbulence dynamics by machine learning
Published in Phys. Rev. Fluids, 2024
Keywords: Turbulence, Turbulence modeling, Machine learning, shell model, Reservoir computing
Recommended citation: S. Matsumoto, M. Inubushi & S. Goto, Phys. Rev. Fluids, 9 (10), 104601 (2024).
talks
Extensive parameter survey for verifying feasibility of turbulence model by machine learning
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talks_ja
乱流混合層における秩序構造の維持機構の解明に向けた直接数値シミュレーション
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リザバーコンピューティングを用いた３次元周期箱乱流のモデル化
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超膨潤ラメラ相の複屈折を利用した流れの可視化
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リカレントニューラルネットワークを用いた乱流モデリング
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機械学習を用いた乱流モデルの実現可能性
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機械学習を用いた乱流モデルの安定性
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データ駆動型クロージャモデリング
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データ駆動型乱流モデルの安定性
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