Ensemble inversion of time-dependent core flow models

Time - Dependent Simulation of Cavitating Flow with k − ` Turbulence Models

A compressible, multiphase, one-fluid RANS solver has been developed to study turbulent cavitating flows. The interplay between turbulence and cavitation regarding the unsteadiness and structure of the flow is complex and not well understood. This constitutes a critical point to accurately simulate the dynamic behaviour of sheet cavities. In the present study, different formulations based on a ...

Iterated Time Series Prediction with Ensemble Models

We describe the use of ensemble methods to build proper models time series prediction. Our approach extends the classical ensemble methods for neural networks by using several different model architectures. We further suggest an iterated prediction procedure to select the final ensemble members. This is an extension of well know the crossvalidation scheme for model validation.

Ensemble Learning For Machine Comprehension: Bidirectional Attention Flow Models

In this paper, we will explore machine comprehension in Stanford Question and Answering Dataset using ensembled deep recurrent neural networks with bi-directional attention flow. Given a context paragraph, we attempt to answer a query related to the context paragraph. This requires use to not only generate knowledge representation for each question and paragraph, but also create mechanisms that...

Minimizing Makespan with Start Time Dependent Jobs in a Two Machine Flow Shop

[if gte mso 9]> The purpose of this paper is to consider the problem of scheduling a set of start time-dependent jobs in a two-machine flow shop, in which the actual processing times of jobs increase linearly according to their starting time. The objective of this problem is to minimize the makespan. The problem is known to be NP-hardness[ah1] ; therefore, there is no polynomial-time algorithm...

Space-time models with time-dependent covariates