『The AIGS Pod』のカバーアート

The AIGS Pod

The AIGS Pod

著者： The AIGS
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The AIGS podcast tackling AI in earth system modeling and more. 博物学 科学 自然・生態学
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  • AI weather models independently learned physics

    AI weather models independently learned physics
    2026/08/30
    Large data-driven physics models like DeepMind's weather model GraphCast have empirically succeeded in parameterizing time operators for complex dynamical systems with an accuracy reaching or in some cases exceeding that of traditional physics-based solvers. Unfortunately, how these data-driven models perform computations is largely unknown and whether their internal representations are interpretable or physically consistent is an open question. Here, the authors adapt tools from interpretability research in Large Language Models to analyze intermediate computational layers in GraphCast, leveraging sparse autoencoders to discover interpretable features in the neuron space of the model. They uncover distinct features on a wide range of length and time scales that correspond to tropical cyclones, atmospheric rivers, diurnal and seasonal behavior, large-scale precipitation patterns, specific geographical coding, and sea-ice extent, among others. They further demonstrate how the precise abstraction of these features can be probed via interventions on the prediction steps of the model. As a case study, they sparsely modify a feature corresponding to tropical cyclones in GraphCast and observe interpretable and physically consistent modifications to evolving hurricanes. Such methods offer a window into the black-box behavior of data-driven physics models and are a step towards realizing their potential as trustworthy predictors and scientifically valuable tools for discovery. Paper: https://arxiv.org/abs/2512.24440
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    42 分
  • GraphCast learns the laws of physics

    GraphCast learns the laws of physics
    2026/08/29
    Large data-driven physics models like DeepMind's weather model GraphCast have empirically succeeded in parameterizing time operators for complex dynamical systems with an accuracy reaching or in some cases exceeding that of traditional physics-based solvers. Unfortunately, how these data-driven models perform computations is largely unknown and whether their internal representations are interpretable or physically consistent is an open question. Here, the authors adapt tools from interpretability research in Large Language Models to analyze intermediate computational layers in GraphCast, leveraging sparse autoencoders to discover interpretable features in the neuron space of the model. They uncover distinct features on a wide range of length and time scales that correspond to tropical cyclones, atmospheric rivers, diurnal and seasonal behavior, large-scale precipitation patterns, specific geographical coding, and sea-ice extent, among others. They further demonstrate how the precise abstraction of these features can be probed via interventions on the prediction steps of the model. As a case study, they sparsely modify a feature corresponding to tropical cyclones in GraphCast and observe interpretable and physically consistent modifications to evolving hurricanes. Such methods offer a window into the black-box behavior of data-driven physics models and are a step towards realizing their potential as trustworthy predictors and scientifically valuable tools for discovery. Paper: https://arxiv.org/abs/2512.24440
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    20 分
  • Walrus masters physics from stars to oceans

    Walrus masters physics from stars to oceans
    2026/08/16
    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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    17 分
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