Foundation models for spatiotemporal systems

2024-11-14 — 2024-11-14

dynamical systems
machine learning
neural nets
sciml
SDEs
signal processing
spatial
stochastic processes
time series

Suspiciously similar content

Figure 1

Foundation models for the whole planet, as opposed to classical NNs, as opposed to classical geospatial techniques.

1 Via modelling the governing equations

See foundation models for PDEs.

2 Incoming

3 References

Allen, Markou, Tebbutt, et al. 2025. “End-to-End Data-Driven Weather Prediction.”
Bodnar, Bruinsma, Lucic, et al. 2024. “Aurora: A Foundation Model of the Atmosphere.”
Chen, Kun, Bai, Ling, et al. 2024. “Towards an End-to-End Artificial Intelligence Driven Global Weather Forecasting System.”
Cheng, Min, Liu, et al. 2025. “TorchDA: A Python Package for Performing Data Assimilation with Deep Learning Forward and Transformation Functions.” Computer Physics Communications.
Chen, Kang, Han, Gong, et al. 2023. “FengWu: Pushing the Skillful Global Medium-Range Weather Forecast Beyond 10 Days Lead.”
Chen, Lei, Zhong, Zhang, et al. 2023. “FuXi: A Cascade Machine Learning Forecasting System for 15-Day Global Weather Forecast.” Npj Climate and Atmospheric Science.
Duraisamy, Iaccarino, and Xiao. 2019. “Turbulence Modeling in the Age of Data.” Annual Review of Fluid Mechanics.
Grohs, and Herrmann. 2022. “Deep Neural Network Approximation for High-Dimensional Elliptic PDEs with Boundary Conditions.” IMA Journal of Numerical Analysis.
Guibas, Mardani, Li, et al. 2021. “Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.”
Hoffimann, Zortea, de Carvalho, et al. 2021. “Geostatistical Learning: Challenges and Opportunities.” Frontiers in Applied Mathematics and Statistics.
Huang, Gianinazzi, Yu, et al. 2024. “DiffDA: A Diffusion Model for Weather-Scale Data Assimilation.”
Keisler. 2022. “Forecasting Global Weather with Graph Neural Networks.”
Keller, and Potthast. 2024. “AI-Based Data Assimilation: Learning the Functional of Analysis Estimation.”
Lam, Sanchez-Gonzalez, Willson, et al. 2023a. “GraphCast: Learning Skillful Medium-Range Global Weather Forecasting.”
———, et al. 2023b. “Learning Skillful Medium-Range Global Weather Forecasting.” Science.
Mahesh, Collins, Bonev, et al. 2024. “Huge Ensembles Part I: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators.”
Manshausen, Cohen, Pathak, et al. 2024. “Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales.”
McNally, Lessig, Lean, et al. 2024. “Data Driven Weather Forecasts Trained and Initialised Directly from Observations.”
Mickisch, Klemmer, Teng, et al. 2025. “A Joint Space-Time Encoder for Geographic Time-Series Data.” In.
Pathak, Subramanian, Harrington, et al. 2022. “Fourcastnet: A Global Data-Driven High-Resolution Weather Model Using Adaptive Fourier Neural Operators.”
Price, Sanchez-Gonzalez, Alet, et al. 2024. “GenCast: Diffusion-Based Ensemble Forecasting for Medium-Range Weather.”
Rasp, Hoyer, Merose, et al. 2024. “WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models.”
Schmude, Roy, Trojak, et al. 2024. “Prithvi WxC: Foundation Model for Weather and Climate.”
Wei, Bora, Oommen, et al. 2025. “XAI4Extremes: An Interpretable Machine Learning Framework for Understanding Extreme-Weather Precursors Under Climate Change.”
Willig, Zečević, Dhami, et al. 2022. “Can Foundation Models Talk Causality?”