Movement paths are used widely in intelligent transportation and smart c...
Crystal property prediction is a crucial aspect of developing novel
mate...
Due to the sweeping digitalization of processes, increasingly vast amoun...
Multivariate time series forecasting constitutes important functionality...
Physics-Informed Neural Networks (PINNs) have recently been proposed to ...
Sensors in cyber-physical systems often capture interconnected processes...
The continued digitization of societal processes translates into a
proli...
A variety of real-world applications rely on far future information to m...
Time series data occurs widely, and outlier detection is a fundamental
p...
In step with the digitalization of transportation, we are witnessing a
g...
Traffic time series forecasting is challenging due to complex spatio-tem...
Correlated time series (CTS) forecasting plays an essential role in many...
With the sweeping digitalization of societal, medical, industrial, and
s...
Path representations are critical in a variety of transportation
applica...
We consider a setting where multiple entities inter-act with each other ...
Origin-destination (OD) matrices are often used in urban planning, where...
Cyber-physical systems often consist of entities that interact with each...
Motivated by the increasing availability of vehicle trajectory data, we
...