Papers using the benchmark

Here we give an overview of all the papers that have used the DynaBench benchmark in their research.

DynaBench

Full title: DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data.

The original DynaBench paper can be found here.

Abstract Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world k nowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available.

GrINd

Full title: GrINd: GrINd: Grid Interpolation Network for Scattered Observations

The original GrINd paper can be found on Arxiv. The original code is available on GitHub.

Abstract Predicting the evolution of spatiotemporal physical systems from sparse and scattered observational data poses a significant challenge in various scientific domains. Traditional methods rely on dense grid-structured data, limiting their applicability in scenarios with sparse observations. To address this challenge, we introduce GrINd (Grid Interpolation Network for Scattered Observations), a novel network architecture that leverages the high-performance of grid-based models by mapping scattered observations onto a high-resolution grid using a Fourier Interpolation Layer. In the high-resolution space, a NeuralPDE-class model predicts the system’s state at future timepoints using differentiable ODE solvers and fully convolutional neural networks parametrizing the system’s dynamics. We empirically evaluate GrINd on the DynaBench benchmark dataset, comprising six different physical systems observed at scattered locations, demonstrating its state-of-the-art performance compared to existing models. GrINd offers a promising approach for forecasting physical systems from sparse, scattered observational data, extending the applicability of deep learning methods to real-world scenarios with limited data availability.

Your paper missing

Have you used the DynaBench benchmark in your paper and cannot find it here? Please let us know by creating a pull request or an issue on our GitHub repository.