2 citations · 2 across the 1 of their papers we have counts for
3 papers
ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii +5
We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent. Massive stars with a Zero Age Ma…
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task
Siavash Golkar, Alberto Bietti, Mariel Pettee +12
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper int…
Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii +4
Some stars are known to explode at the end of their lives, called supernovae (SNe). The substantial amount of matter and energy that SNe release provides significant feedback to st…