Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Morimoto, Masaki"'
Autor:
Shimonosono, Masataka1 (AUTHOR) k5541938@kadai.jp, Morimoto, Masaki1 (AUTHOR) mm6093@cumc.columbia.edu, Hirose, Wataru1 (AUTHOR) wh2564@cumc.columbia.edu, Tomita, Yasuto1 (AUTHOR) yt2795@cumc.columbia.edu, Matsuura, Norihiro1 (AUTHOR) mm5075mn@yahoo.co.jp, Flashner, Samuel1 (AUTHOR) sf3070@cumc.columbia.edu, Ebadi, Mesra S.1 (AUTHOR) mse2144@barnard.edu, Okayasu, Emilea H.1 (AUTHOR) eho2122@barnard.edu, Lee, Christian Y.1 (AUTHOR) cl4316@columbia.edu, Britton, William R.1 (AUTHOR) wrb2120@cumc.columbia.edu, Martin, Cecilia1,2 (AUTHOR) cm4194@cumc.columbia.edu, Wuertz, Beverly R.3 (AUTHOR) knier003@umn.edu, Parikh, Anuraag S.1,4 (AUTHOR) asp2145@cumc.columbia.edu, Sachdeva, Uma M.5 (AUTHOR) uma.sachdeva@mgh.harvard.edu, Ondrey, Frank G.3 (AUTHOR) ondre002@umn.edu, Atigadda, Venkatram R.6 (AUTHOR) venkatra@uab.edu, Elmets, Craig A.6 (AUTHOR) celmets@uabmc.edu, Abrams, Julian A.1,7 (AUTHOR) ja660@cumc.columbia.edu, Muir, Amanda B.8 (AUTHOR) muira@chop.edu, Klein-Szanto, Andres J.9 (AUTHOR) andres.klein-szanto@fccc.edu
Publikováno v:
Biomolecules (2218-273X). Sep2024, Vol. 14 Issue 9, p1126. 16p.
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 approximates a posterior Gaussian distribution of each weight, given tra
Externí odkaz:
http://arxiv.org/abs/2109.08248
Autor:
Masuda, Mia Y., Pyon, Grace C., Luo, Huijun, LeSuer, William E., Putikova, Arina, Dao, Adelyn, Ortiz, Danna R., Schulze, Aliviya R., Fritz, Nicholas, Kobayashi, Takao, Iijima, Koji, Klein-Szanto, Andres J., Shimonosono, Masataka, Flashner, Samuel, Morimoto, Masaki, Pai, Rish K., Rank, Matthew A., Nakagawa, Hiroshi, Kita, Hirohito, Wright, Benjamin L., Doyle, Alfred D.
Publikováno v:
In The Journal of Allergy and Clinical Immunology May 2024 153(5):1355-1368
Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows
Autor:
Moriya, Naoki, Fukami, Kai, Nabae, Yusuke, Morimoto, Masaki, Nakamura, Taichi, Fukagata, Koji
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 with a first grid point set relatively far from the wall ($\sim$ 10 wall
Externí odkaz:
http://arxiv.org/abs/2106.09271
Publikováno v:
SN Comput. Sci. 5, 306 (2024)
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 understanding of their complex nature. This trend can be found in a wide
Externí odkaz:
http://arxiv.org/abs/2103.09020
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 considering some canonical regression problems with fluid flow data.
Externí odkaz:
http://arxiv.org/abs/2101.02535
Publikováno v:
Neural Comput. Appl. 34, 3647-3669 (2022)
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 for applications of machine learning to fluid dynamics are considered:
Externí odkaz:
http://arxiv.org/abs/2011.11911
Publikováno v:
SN Comput. Sci. 2, 467 (2021)
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 neural network and multi-layer perceptrons is considered in this study
Externí odkaz:
http://arxiv.org/abs/2011.10277
Publikováno v:
Phys. Fluids, Vol. 33, Paper 087121 (2021)
We propose a method using supervised machine learning to estimate velocity fields from particle images having missing regions due to experimental limitations. As a first example, a velocity field around a square cylinder at Reynolds number of ${\rm R
Externí odkaz:
http://arxiv.org/abs/2005.00756
Autor:
Hara, Kazushi, Horikoshi, Yosuke, Morimoto, Masaki, Nakaso, Kazuhiro, Sunaguchi, Teppei, Kurashiki, Tatsuyuki, Nakayama, Yuji, Hanaki, Takehiko, Yamamoto, Manabu, Sakamoto, Teruhisa, Fujiwara, Yoshiyuki, Matsura, Tatsuya
Publikováno v:
In Translational Oncology February 2023 28