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Found 9 papers, 7 papers with code
Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data
The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms.
Physics and Society
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment.
Data-driven generation of spatio-temporal routines in human mobility
DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory.
The agglomeration and dispersion dichotomy of human settlements on Earth
To explain the observed spatial patterns, we also propose a model that combines two agglomeration forces and simulates human settlements' historical growth.
Physics and Society
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design.
Social media usage reveals how regions recover after natural disaster
The challenge of nowcasting and forecasting the effect of natural disasters (e. g. earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme events.
Physics and Society Computers and Society Applications
Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications
At a high level, we conclude that on NVIDIA systems: (1) confounding bottlenecks such as memory inefficiency often dominate runtime costs moreso than data sparsity alone, (2) native Pytorch operations are often as or more competitive than their Pytorch Geometric equivalents, especially at low to moderate levels of input data sparsity, and (3) many operations central to state-of-the-art GNN architectures have little to no optimization for sparsity.
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems
This end-to-end log analysis system, coupled with visual analytics support, allows users to glean and promptly extract supercomputer usage and error patterns at varying temporal and spatial resolutions.
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations.
