Search Results - Hirashima, Keiya

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  1. 1
    Predicting the Expansion of Supernova Shells Using Deep Learning toward Highly Resolved Galaxy Simulations

    Predicting the Expansion of Supernova Shells Using Deep Learning toward Highly Resolved Galaxy Simulations by Hirashima, Keiya, Moriwaki, Kana, Fujii, Michiko, Hirai, Yutaka, Saitoh, Takayuki, Makino, Junichiro

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  2. 2
    3D-Spatiotemporal forecasting the expansion of supernova shells using deep learning towards high-resolution galaxy simulations

    3D-Spatiotemporal forecasting the expansion of supernova shells using deep learning towards high-resolution galaxy simulations by Hirashima, Keiya, Moriwaki, Kana, Fujii, Michiko S, Hirai, Yutaka, Saitoh, Takayuki R, Makino, Junichiro

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  3. 3
    ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback

    ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback by Hirashima, Keiya, Moriwaki, Kana, Fujii, Michiko S, Hirai, Yutaka, Saitoh, Takayuki R, Makino, Junnichiro, Steinwandel, Ulrich P, Ho, Shirley

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  4. 4
    Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations

    Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations by Hirashima, Keiya, Moriwaki, Kana, Fujii, Michiko S, Hirai, Yutaka, Saitoh, Takayuki R, Makino, Junichiro, Ho, Shirley

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  5. 5
    3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations

    3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations by Hirashima, Keiya, Moriwaki, Kana, Fujii, Michiko S, Hirai, Yutaka, Saitoh, Takayuki R, Makino, Junichiro

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  6. 6
    Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task

    Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task by Golkar, Siavash, Bietti, Alberto, Pettee, Mariel, Eickenberg, Michael, Cranmer, Miles, Hirashima, Keiya, Geraud Krawezik, Lourie, Nicholas, McCabe, Michael, Morel, Rudy, Ohana, Ruben, Liam Holden Parker, Bruno Régaldo-Saint Blancard, Cho, Kyunghyun, Ho, Shirley

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  7. 7
    The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning

    The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning by Ohana, Ruben, McCabe, Michael, Meyer, Lucas, Morel, Rudy, Agocs, Fruzsina J, Beneitez, Miguel, Berger, Marsha, Burkhart, Blakesley, Dalziel, Stuart B, Fielding, Drummond B, tunato, Daniel, Goldberg, Jared A, Hirashima, Keiya, Yan-Fei, Jiang, Kerswell, Rich R, Suryanarayana Maddu, Miller, Jonah, Mukhopadhyay, Payel, Nixon, Stefan S, Shen, Jeff, Watteaux, Romain, Bruno Régaldo-Saint Blancard, Rozet, François, Parker, Liam H, Cranmer, Miles, Ho, Shirley

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