AI4Science
RealPDEBench
RealPDEBench
RealPDEBench is a benchmark of paired real-world measurements and matched numerical simulations for complex physical systems. It is designed for spatiotemporal forecasting and sim-to-real transfer evaluation on real data.
This Hub repository (AI4Science-WestlakeU/RealPDEBench) is the release repo for RealPDEBench.
Website & documentation: realpdebench.github.io
Raw HDF5 distribution: realpdebench.westlake.edu.cn
Benchmark codebase:… See the full description on the dataset page: https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDEBench.core-sdo
ML-Ready Multi-Modal Image Dataset from SDO
Overview
This dataset provides machine learning (ML)-ready solar data curated from NASA’s Solar Dynamics Observatory (SDO), covering observations from May 13, 2010, to Dec 31, 2024. It includes Level-1.5 processed data from: Atmospheric Imaging Assembly (AIA)
and Helioseismic and Magnetic Imager (HMI).
The dataset is designed to facilitate large-scale learning applications in heliophysics, such as space weather forecasting… See the full description on the dataset page: https://huggingface.co/datasets/nasa-ibm-ai4science/core-sdo.Surya-1.0_validation_data
Validation data for Surya 1.0
This dataset comprises imagery from NASA's Solar Dynamics Observatory (SDO). The data can and should be used to validate a local installation of the Surya Foundation Model for Heliophysics. The data is compressed; you should use the hdf5plugin to read it directly.
RealPDE-Competition-Data
RealPDE Competition Data (NeurIPS 2026)
Training data and baseline checkpoints for the NeurIPS 2026 RealPDE
Competition. This is a mirror of the
competition's Google Drive release, hosted here because the Drive link runs into
a per-file anonymous download quota when many people fetch it at once.
Both tracks share this release:
Track 1, Sim2Real — codabench.org/competitions/17363
Track 2, LTTTA — codabench.org/competitions/17385
Contents
train_sim.tar.gz… See the full description on the dataset page: https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDE-Competition-Data.Sombench-Ice-Prospectivity-Regression
SomBench Benchmark: Polar Ice Prospectivity Regression
Science theme: Polar volatiles
Task: Regression
Dataset Summary
A polar, multi-layer benchmark for predicting near-surface water-ice
prospectivity within ~10° latitude of each pole at 240 m/pixel. Following
the ice-prospectivity workflow of Coyan et al. (2025), the dataset includes a
group of physically motivated evidential layers (thermophysical,
illumination, and terrain) alongside a continuous prospectivity… See the full description on the dataset page: https://huggingface.co/datasets/nasa-ibm-ai4science/Sombench-Ice-Prospectivity-Regression.Sombench-pretraining-data
SomBench Pre-training Corpus: Multimodal Lunar Tiles
Dataset Summary
This includes a small sample from SomBench: a corpus of co-registered, multimodal lunar image tiles built for
large-scale self-supervised (foundation-model) pre-training. It contains a subset of modalities from the
low-resolution (WAC-anchored) and high-resolution (NAC-anchored) tracks specifically used in pretraining.
Tiles are anchored to individual LROC Experiment Data Record (EDR) image… See the full description on the dataset page: https://huggingface.co/datasets/nasa-ibm-ai4science/Sombench-pretraining-data.