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Keiya Hirashima

3 accepted papers

2026

Walrus: A Cross-domain Foundation Model for Continuum Dynamics

ICML 2026spotlight

Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unstable long-term dynamics inhibit learning from sufficiently diverse dynamics, while varying resolutions and dimensionalit…

Cited by 0SourceScholar
2025

AION-1: Omnimodal Foundation Model for Astronomical Sciences

NeurIPS 2025poster

While foundation models have shown promise across a variety of fields, astronomy lacks a unified framework for joint modeling across its highly diverse data modalities. In this paper, we present AION-1, the first large-scale multimodal foundation family of models for astronomy. AION-1 enables arbitr…

Cited by 0SourceScholar
2024

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

NeurIPS 2024poster

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we in…