Presentations -
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Towards practical uses of supervised neural networks for fluid flow regression
M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata
[International presentation] Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET 2021),
2021.09,Oral presentation (general)
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流動場の空間再構築のための階層型ニューラルネットワーク
守矢 直樹，森本 将生，深見 開，長谷川 一登，深潟 康二
[Domestic presentation] 日本流体力学会年会2021,
2021.09,Oral presentation (general)
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潜在ベクトルとスパース回帰を用いた流れ場時系列解析：データ駆動型流れ制御に向けて
深見 開，村田 高彬，張 凱，兼平 昇英，深潟 康二
[Domestic presentation] 日本機械学会2021年度年次大会,
2021.09,Oral presentation (general)
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畳み込みニューラルネットワークに基づく非線形モード分解の3次元流れへの応用
長谷川 一登，深見 開，深潟 康二
[Domestic presentation] 日本機械学会2021年度年次大会,
2021.09,Oral presentation (general)
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畳み込みニューラルネットワークを用いたスパースセンサからの流れ場状態推定
中村 太一，深見 開，深潟 康二
[Domestic presentation] 日本機械学会2021年度年次大会,
2021.09,Oral presentation (general)
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畳み込みニューラルネットワークの流体解析への応用
深潟 康二
[Domestic presentation] 自動車技術会 第9回CFD技術部門委員会,
2021.09,Oral presentation (invited, special)
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Applications of CNN autoencoders to fluid mechanics problems
K. Fukagata
[International presentation] RIMS Workshop, Mathematical methods for the studies of flow, shape, and dynamics,
2021.08,Oral presentation (invited, special)
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Toward machine learning based control of turbulence
K. Fukagata
[International presentation] International Congress of Theoretical and Applied Mechanics (25th ICTAM),
2021.08,Oral presentation (invited, special)
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Demonstration of machine learning-based reduced order modeling using unsteady flows around bluff bodies with various shapes
K. Hasegawa, K. Fukami, and K. Fukagata
[International presentation] International Congress of Theoretical and Applied Mechanics (25th ICTAM),
2021.08,Oral presentation (general)
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Extracting nonlinear dynamics of low-dimensionalized flows
K. Fukami, T. Murata, and K. Fukagata
[International presentation] International Congress of Theoretical and Applied Mechanics (25th ICTAM),
2021.08,Oral presentation (general)
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Latent variable-based analysis with machine learning for reduced-order modeling and control of fluid flows
K. Fukami, K. Hasegawa, T. Nakamura, S. Kanehira, and K. Fukagata
[International presentation] 16th U.S. National Congress on Computational Mechanics,
2021.07,Oral presentation (general)
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Error-curve analysis of neural network and linear stochastic estimation for fluid flow problems
T. Nakamura, K. Fukami, and K. Fukagata
[International presentation] 16th U.S. National Congress on Computational Mechanics,
2021.07,Oral presentation (general)
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Parameter influence of supervised/unsupervised use of convolutional neural networks for fluid flow analyses
M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, and K. Fukagata
[International presentation] 16th U.S. National Congress on Computational Mechanics,
2021.07,Oral presentation (general)
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術中の高流量酸素カニュラ使用が患者周囲の酸素濃度に及ぼす影響についての研究
伊東 真吾，関 博志，深潟 康二，岡田 玲奈，大内 貴志
[Domestic presentation] 日本麻酔科学会第68回学術集会,
2021.06,Oral presentation (general)
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畳み込みニューラルネットワークの流体問題への応用
深潟 康二
[Domestic presentation] 日本伝熱学会北陸信越支部春季セミナー,
2021.05,Public lecture, seminar, tutorial, course, or other speech
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Clues for Noise Robustness of State Estimation: Error-curve Quest of Neural Network and Linear Regression
T. Nakamura, K. Fukami, and K. Fukagata
[International presentation] The Ninth International Conference on Learning Representations (ILCR 2021),
2021.05,Oral presentation (general)
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Supervised convolutional networks for volumetric data enrichment from limited sectional data with adaptive super resolution
M. Matsuo, K. Fukami, T. Nakamura, M. Morimoto, and K. Fukagata
[International presentation] The Ninth International Conference on Learning Representations (ILCR 2021),
2021.05,Oral presentation (general)
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Voronoi tessellation-assisted convolutional neural network for flow field reconstruction from sparse sensors
K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira
[International presentation] 14th Southern California Flow Physics Symposium (SoCal Fluids XIV),
2021.04,Oral presentation (general)
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Utilization of autoencoder-based nonlinear manifolds for fluid flow forecasts driven with long short-term memory
T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, and K. Fukagata
[International presentation] DataLearning Working Group Seminar, Imperial College London,
2021.03,Public lecture, seminar, tutorial, course, or other speech
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機械学習を用いた乱流の状態推定：入力ノイズに対するロバスト性
中村 太一，深見 開，深潟 康二
[Domestic presentation] 日本機械学会関東支部第27期総会・講演会,
2021.03,Oral presentation (general)