The AIGS Pod

The AIGS
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The AIGS podcast tackling AI in earth system modeling and more.

  1. 16 ago

    Walrus masters physics from stars to oceans

    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

    Walrus masters physics from stars to oceans

  2. 9 ago

    Walrus AI unifies nineteen physics scenarios

    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

    Walrus AI unifies nineteen physics scenarios

  3. 2 ago

    Teaching AI the laws of physics

    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

    Teaching AI the laws of physics

  4. 26 lug

    Aurora AI beats traditional weather models

    Aurora is a pioneering AI foundation model created by Microsoft to revolutionize Earth system forecasting by delivering high-resolution predictions with unprecedented speed and efficiency. Developed using a 3D Swin Transformer architecture, the model was pretrained on over one million hours of diverse geophysical data to learn the complex dynamics of the atmosphere. Through a process of fine-tuning, Aurora can be specialized for diverse tasks, including weather forecasting, air pollution tracking, ocean wave modeling, and tropical cyclone tracking. Research indicates that it consistently matches or outperforms state-of-the-art numerical models, such as those from the ECMWF, while operating at a computational cost orders of magnitude lower than traditional simulations. The most recent iteration, Aurora 1.5, introduces expanded variables and probabilistic ensemble forecasting to better quantify atmospheric uncertainty. By providing an open-source framework via GitHub, Microsoft aims to democratize access to accurate environmental data and support global efforts in disaster mitigation and climate science. Link: https://www.microsoft.com/en-us/research/project/aurora-forecasting/

    Aurora AI beats traditional weather models

  5. 26 lug

    Microsoft Aurora outperforms traditional weather models

    Aurora is a pioneering AI foundation model created by Microsoft to revolutionize Earth system forecasting by delivering high-resolution predictions with unprecedented speed and efficiency. Developed using a 3D Swin Transformer architecture, the model was pretrained on over one million hours of diverse geophysical data to learn the complex dynamics of the atmosphere. Through a process of fine-tuning, Aurora can be specialized for diverse tasks, including weather forecasting, air pollution tracking, ocean wave modeling, and tropical cyclone tracking. Research indicates that it consistently matches or outperforms state-of-the-art numerical models, such as those from the ECMWF, while operating at a computational cost orders of magnitude lower than traditional simulations. The most recent iteration, Aurora 1.5, introduces expanded variables and probabilistic ensemble forecasting to better quantify atmospheric uncertainty. By providing an open-source framework via GitHub, Microsoft aims to democratize access to accurate environmental data and support global efforts in disaster mitigation and climate science. Link: https://www.microsoft.com/en-us/research/project/aurora-forecasting/

    Microsoft Aurora outperforms traditional weather models

  6. 26 lug

    Aurora AI beats multi-billion dollar supercomputers

    Aurora is a pioneering AI foundation model created by Microsoft to revolutionize Earth system forecasting by delivering high-resolution predictions with unprecedented speed and efficiency. Developed using a 3D Swin Transformer architecture, the model was pretrained on over one million hours of diverse geophysical data to learn the complex dynamics of the atmosphere. Through a process of fine-tuning, Aurora can be specialized for diverse tasks, including weather forecasting, air pollution tracking, ocean wave modeling, and tropical cyclone tracking. Research indicates that it consistently matches or outperforms state-of-the-art numerical models, such as those from the ECMWF, while operating at a computational cost orders of magnitude lower than traditional simulations. The most recent iteration, Aurora 1.5, introduces expanded variables and probabilistic ensemble forecasting to better quantify atmospheric uncertainty. By providing an open-source framework via GitHub, Microsoft aims to democratize access to accurate environmental data and support global efforts in disaster mitigation and climate science. Link: https://www.microsoft.com/en-us/research/project/aurora-forecasting/

    Aurora AI beats multi-billion dollar supercomputers

  7. 21 lug

    NVIDIA ATLAS beats supercomputers on one GPU

    The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fundamental drivers of forecast accuracy. Here, we demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized training heuristics. We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector that resolves high-resolution physics. We find that our framework design is robust to the choice of probabilistic estimator, seamlessly supporting stochastic interpolants, diffusion models, and CRPS-based ensemble training. Validated against the Integrated Forecasting System and the deep learning probabilistic model GenCast, our framework achieves statistically significant improvements on most of the variables. These results suggest scaling a general-purpose model is sufficient for state-of-the-art medium-range prediction, eliminating the need for tailored training recipes and proving effective across the full spectrum of probabilistic frameworks. Preprint: https://arxiv.org/abs/2601.18111

    NVIDIA ATLAS beats supercomputers on one GPU

  8. 21 lug

    NVIDIA ATLAS beats global weather supercomputers

    The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fundamental drivers of forecast accuracy. Here, we demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized training heuristics. We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector that resolves high-resolution physics. We find that our framework design is robust to the choice of probabilistic estimator, seamlessly supporting stochastic interpolants, diffusion models, and CRPS-based ensemble training. Validated against the Integrated Forecasting System and the deep learning probabilistic model GenCast, our framework achieves statistically significant improvements on most of the variables. These results suggest scaling a general-purpose model is sufficient for state-of-the-art medium-range prediction, eliminating the need for tailored training recipes and proving effective across the full spectrum of probabilistic frameworks. Preprint: https://arxiv.org/abs/2601.18111

    NVIDIA ATLAS beats global weather supercomputers
Elenco completo (17)

Descrizione

The AIGS podcast tackling AI in earth system modeling and more.

Informazioni

  • Creatore
    The AIGS
  • Anni di attività
    2026
  • Puntate
    17
  • Classificazione
    Contenuti adatti a tutti
  • Sito web del podcast
    The AIGS Pod