Search Results for author:
Found 5 papers, 2 papers with code
GrINd: Grid Interpolation Network for Scattered Observations
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.
TaylorPDENet: Learning PDEs from non-grid Data
Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models.
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data
The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements.
Do Different Deep Metric Learning Losses Lead to Similar Learned Features?
In this paper, we investigate this by conducting a two-step analysis to extract and compare the learned visual features of the same model architecture trained with different loss functions: First, we compare the learned features on the pixel level by correlating saliency maps of the same input images.
NeuralPDE: Modelling Dynamical Systems from Data
Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs).
