6 citations · 10 across the 8 of their papers we have counts for
9 papers
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
Jeff Shen, Francois Lanusse, Liam Holden Parker +24
Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Ast…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
Self-supervised Synthetic Pretraining for Inference of Stellar Mass Embedded in Dense Gas
Keiya Hirashima, Shingo Nozaki, Naoto Harada
Stellar mass is a fundamental quantity that determines the properties and evolution of stars. However, estimating stellar masses in star-forming regions is challenging because youn…
The First Star-by-star -body/Hydrodynamics Simulation of Our Galaxy Coupling with a Surrogate Model
Keiya Hirashima, Michiko S. Fujii, Takayuki R. Saitoh +9
A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-sca…
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Liam Parker, Francois Lanusse, Jeff Shen +24
While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In th…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
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…
3D-Spatiotemporal Forecasting the Expansion of Supernova Shells Using Deep Learning toward High-Resolution Galaxy Simulations
Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii +3
Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for…