… and weight-constrained RNN models that account for process structural knowledge by
modifying the structure of RNNs, and adding certain constraints in the training process of RNNs, …

… of the identified model. This study compares the performance of three kinds of different
model structures with respect to their ability to represent the rainfall-runoff process of a medium…

… process modeling literature is timely and valuable. In this work, we focus on the activity
network-based process models that … on the purposes of PD process models: project visualization, …

… of process dynamics by combining mechanistic and neural network models in such a way that
the neural network model … model. In this work, a full-scale coke-plant wastewater treatment …

… We believe that it is advantageous to a priori structure the neural network models; in … the
final model. In this article we follow a variation of this approach and develop a modeling strategy …

… network models for RT and elementary mental processes. The class of models, in their
most general form, are continuous-transmission-network models. As is shown below, the …

… This article presents models in which networks of relational ties are assumed to depend on
the attributes of the actors who are members of the network. We refer to these as models for …

… of these implicit networks, we develop a probabilistic model that combines mutuallyexciting
point processes with random … We evaluate this new model empirically on several datasets. …

… So, the process of building good simulation models is … the network modeling project life
cycle and expose the reader to some of the particular considerations when modeling network …

… techniques promise to improve our understanding of each of these processes and ultimately…
, computational network models have begun to identify novel targets in regulatory networks …