『Walrus masters physics from stars to oceans』のカバーアート

Walrus masters physics from stars to oceans

Walrus masters physics from stars to oceans

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Foundation models have reshaped language and vision, but physical simulation has resisted the same playbook: heterogeneous data, unstable long-term rollouts, and mismatched resolutions and dimensionalities make it hard to train one model across many kinds of physics. This paper introduces Walrus, a transformer-based foundation model for fluid-like continuum dynamics, built around a harmonic-analysis-based stabilization method, load-balanced distributed 2D and 3D training strategies, and compute-adaptive tokenization. Walrus is pretrained on nineteen diverse scenarios spanning astrophysics, geoscience, rheology, plasma physics, acoustics, and classical fluids, treating Earth's atmosphere and oceans as just one flavor of continuum dynamics among many. Across short- and long-horizon forecasts on downstream tasks, and across the full breadth of pretraining domains, Walrus outperforms prior foundation models, while ablation studies confirm that the stabilization, distributed training, and tokenization choices each meaningfully improve forecast stability, training throughput, and transfer performance. Code and weights are released for community use. Paper: https://arxiv.org/abs/2511.15684
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