Generative modeling of turbulence

2DV Nonlinear k-ε Turbulence Modeling of Stratified Flows

The commonly used linear k-ε turbulence model is shown to be incapable of accurate prediction of turbulent flows, where non-isotropy is dominant. Two examples of non-isotropic flows, which have a wide range of applications in marine waters, are saline water flow and the stratified flows due to temperature gradients. These relate to stratification and consequently, variation of density through...

Turbulence Modeling

Generative Modeling by PRISM

PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic modeling capable of learning statistical parameters from observed data. After reviewing it from various viewpoints, we examine some technical details related to logic programming, including semantics, search and program synthesis.

Generative mesh modeling

We propose a novel model representation method. Its main feature is that 3D shapes are represented in terms of functions instead of geometric primitives. Given a set of – typically only a few – specific parameters, evaluating such a function results in a model that is one instance of a general shape. The shape description language is a full programming language, but it has an extremely simple s...

Lifelong Generative Modeling

Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to generative modeling where we continuously incorporate newl...