Earth & Environment

Computational Fluid Dynamics

A large-scale CFD-generated wind field simulation dataset containing over 12,000 cases, designed for training and evaluating terrain-informed machine learning models to reconstruct fine-scale 3D wind fields

CFD

Large-scale CFD-generated wind field dataset with over 12,000 cases, designed for training and evaluating the terrain-informed machine learning model FuXi-CFD for fine-scale 3D wind field reconstruction.

Hydrology Data

A daily average discharge dataset from Ait Melloul hydrological station in Morocco spanning 1970 to 2024, sourced from Morocco Open Data, containing 34 rows and provided in ML-ready Parquet format.

Neural Operator

A neural operator model based on spherical Fourier transforms, designed for learning partial differential equations on spherical geometries.

CFD

NVIDIA Earth-2 leverages AI atmospheric models integrated with observation data to provide high-resolution weather forecasts and decision support for weather-sensitive industries.

Geophysics

NVIDIA Earth-2 enables weather-sensitive industries like energy to make timely decisions by integrating observational data with AI-powered atmospheric simulation.

Weather / Large-Scale Physical Prediction

A graph neural network for medium-range weather prediction that outperforms conventional numerical weather prediction on many targets while running in minutes instead of hours.

Geophysics / Inverse Problem

LANL's large-scale FWI benchmark - 12 datasets, 2.1 TB - reconstructing subsurface velocity from seismic data with data-driven methods, serving carbon storage, reservoir and earthquake applications.

Geophysics

This paper presents a domain-decomposed random-feature evolutional deep neural network (DD RF-EDNN) for solving transient pressure diffusion in heterogeneous porous media with discontinuous and high-contrast permeability, combining spatial approximation with temporal evolution.

CFD

Proposes a physics-guided hybrid architecture combining UNet and FNO, integrating multi-modal Sentinel-1/2 remote sensing with shallow water equation constraints for end-to-end flood prediction.

CFD/Fluid Dynamics

A Transformer-based neural operator approach for 3D wind field prediction over complex mountainous terrain, addressing bottlenecks in traditional CFD methods that require expert-intensive mesh generation and computationally expensive iterative solvers.

Geophysics

A continental-scale electrical resistivity model of Australia's shallow crust (up to ~650 m depth) derived from probabilistic deep learning inversion of 26.6+ million AEM soundings, with quantified uncertainty for geology, groundwater, and critical mineral exploration.

Geophysics

Proposes a three-step framework for ensemble smoother with multiple data assimilation (ES-MDA), unifying existing adaptive variants to balance accuracy and computational cost in reservoir engineering inverse problems.

Geophysics

A continually learning neural-operator surrogate model replaces the traditional forward solver for efficient Bayesian inversion of three-dimensional time-domain airborne electromagnetic data, addressing prohibitive computational costs.

CFD

Proposes WaveOp-LiteFM, a lightweight neural-operator flow matching approach that addresses the trade-off between computational cost and accuracy in satellite-to-radar precipitation retrieval for atmospheric remote sensing applications.

Electromagnetics

Proposes a hybrid CNN-Transformer architecture for solar flare prediction based on the physical process of magnetic energy release in the solar atmosphere, enabling early warning for C/M/X-class flares.

Fluid Mechanics

This study evaluates five deep learning architectures for univariate significant wave height forecasting using buoy observations. Across a 47-buoy, 37-year evaluation corpus, the model families converge to nearly identical performance, with an between-family standard deviation of 0.0014 square meters.