3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations. - Keiya Hirashima; Kana Moriwaki; Michiko S. Fujii; Yutaka Hirai; Takayuki R. Saitoh; Junichiro Makino (CoRR, 2023)

Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations. - Keiya Hirashima; Kana Moriwaki; Michiko S. Fujii; Yutaka Hirai; Takayuki R. Saitoh; Junichiro Makino; Shirley Ho (CoRR, 2023)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. - Ruben Ohana; Michael McCabe; Lucas Meyer; Rudy Morel; Fruzsina J. Agocs; Miguel Beneitez; Marsha Berger; Blakesley Burkhart; Stuart B. Dalziel; Drummond B. Fielding; Daniel Fortunato; Jared A. Goldberg; et al. (NeurIPS, 2024)

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. - Siavash Golkar; Alberto Bietti; Mariel Pettee; Michael Eickenberg; Miles D. Cranmer; Keiya Hirashima; Géraud Krawezik; Nicholas Lourie; Michael McCabe; Rudy Morel; Ruben Ohana; Liam Holden Parker; et al. (CoRR, 2024)

ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback. - Keiya Hirashima; Kana Moriwaki; Michiko S. Fujii; Yutaka Hirai; Takayuki R. Saitoh; Junnichiro Makino; Ulrich P. Steinwandel; Shirley Ho (CoRR, 2024)