Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions.
In this paper, two machine learning algorithms, from the family of deep generative models, are proposed for the problem of population synthesis and with particular attention to the problem of sampling zeros.
We use the presented approach to reveal the dynamics of transport preferences for a fixed pseudo panel of individuals based on a large Danish cross-sectional data set covering the period from 2006 to 2016.
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise.
Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations.
It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models.
We present an online graphical pattern search tool for electronic band structure data contained within the Organic Materials Database (OMDB) available at http://omdb. diracmaterials. org/search/pattern.