The AIGS Pod
The AIGS podcast tackling AI in earth system modeling and more.
Episodes
October 16, 2026 • 23 mins
Finding that a network encodes something is not the same as knowing how it uses it. Edge-based circuits have been defined for large language models, but work on vision models has so far only used neuron-based circuits, which show what is encoded but not how it is routed. The authors introduce Automatic Visual Circuit Discovery (Vi-CD), which finds sparse, edge-based computational subgraphs in vision transformers. They apply it to c...
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October 12, 2026 • 43 mins
Could it be that AI weather models are solving physical equations, although they may not be the equations used by conventional NWP models? The authors compute correlations of forecast skill and Centered Kernel Alignment, providing evidence that different AI weather models represent the atmosphere in similar ways, despite differences in architecture and capacity. They argue that the architecture and training of the AI models constra...
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September 12, 2026 • 53 mins
Artificial Intelligence (AI) weather models are improving rapidly, and their forecasts are already competitive with long-established traditional Numerical Weather Prediction (NWP). To build confidence in this new methodology, it is critical to understand how these predictions are generated. This is a huge challenge as these AI weather models remain largely black boxes. In other areas of Machine Learning (ML), mechanistic interpreta...
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September 11, 2026 • 23 mins
Artificial Intelligence (AI) weather models are improving rapidly, and their forecasts are already competitive with long-established traditional Numerical Weather Prediction (NWP). To build confidence in this new methodology, it is critical to understand how these predictions are generated. This is a huge challenge as these AI weather models remain largely black boxes. In other areas of Machine Learning (ML), mechanistic interpreta...
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September 7, 2026 • 23 mins
Deep learning weather prediction models achieve remarkable predictive skill yet remain largely opaque: little is known about how they represent physical climate phenomena internally. Mechanistic interpretability through Sparse Autoencoders (SAEs) offers a principled route to decomposing these representations, but existing SAEs assume strictly linear feature superposition - a constraint ill-suited for the highly nonlinear atmospheri...
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August 29, 2026 • 42 mins
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 ...
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August 28, 2026 • 20 mins
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 ...
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August 15, 2026 • 17 mins
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...
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August 8, 2026 • 21 mins
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...
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August 1, 2026 • 18 mins
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...
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July 25, 2026 • 45 mins
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 di...
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July 25, 2026 • 23 mins
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 di...
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July 25, 2026 • 19 mins
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 di...
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July 20, 2026 • 22 mins
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 atmo...
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July 20, 2026 • 60 mins
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 atmo...
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July 14, 2026 • 21 mins
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), research shows that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill. This work examines whether t...
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July 5, 2026 • 20 mins
A comprehensive review of climate model emulation methods and their validation. This episode discusses the latest survey from Claudia Tebaldi, Noelle E. Selin, Rafael Ferrari, and Glenn Flierl, published in the Annual Review of Environment and Resources (2025). The paper surveys modern approaches to climate model emulation—surrogate models that replicate Earth System Model behavior at a fraction of the computational cost. Key...
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June 29, 2026 • 21 mins
Machine-learning weather-climate emulators like the Ai2 Climate Emulator (ACE) can reproduce the climate of recent decades, but they break down in unfamiliar conditions, producing unphysical results when asked to simulate a world with sea surface temperatures warmed by +4 K or an abrupt quadrupling of CO2. This research traces the problem to training data in which sea surface temperature (SST) and CO2 are tightly correlated, leavin...
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June 29, 2026 • 60 mins
Machine-learning weather-climate emulators like the Ai2 Climate Emulator (ACE) can reproduce the climate of recent decades, but they break down in unfamiliar conditions, producing unphysical results when asked to simulate a world with sea surface temperatures warmed by +4 K or an abrupt quadrupling of CO2. This research traces the problem to training data in which sea surface temperature (SST) and CO2 are tightly correlated, leavin...
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May 25, 2026 • 19 mins
A look at the growing body of work questioning where AI weather and climate models fall short, and why physics-based simulation remains essential. Papers discussed: Zhang et al. (2026), Physics-based models outperform AI weather forecasts of record-breaking extremes, Science Advances, https://www.science.org/doi/full/10.1126/sciadv.aec1433 — Smith & Thorpe (2026), The Primacy of Physical Simulation in the Age of AI: A Cri...
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