Output Error Estimation for Unsteady Flows Using Reconstructed Solutions: Effect of Compression and Reconstruction of Unsteady CFD Data using Neural Networks and PODs on Error Estimates

Unsteady numerical simulation has been proven to be an essential tool for research. The quality of the results can be improved by using mesh adaptation. Mesh adaptation uses error indicators to refine the mesh in regions with high errors. The error indicators used are output errors with the most accurate output error estimation method being...

Finding a rich lower dimensional representation

Artificial neural networks are the key driver of progress in various semantic computer vision tasks such as age prediction and digit classification. For the successful application of neural network algorithms, the representation of the data is an important factor. A good representation can significantly simplify a regression or prediction task....

Using neural networks to model the behavior in vessel trajectories

The contemporary trend shows a shift from rule-based algorithms to deep learning. In the last few years, this field has been developing rapidly and its popularity has increased to a large extent. This happened for a good reason, since deep learning was able to solve some of the hardest problems in fields like image recognition, natural language...

Latent Space Modelling of Unsteady Flow Subdomains: Thesis Report

Very complex flows can be expensive to compute using current CFD techniques. In this thesis, models based on deep learning were used to replace certain parts of the flow domain, with the objective of replacing well-known regions with simplified models to increase efficiency. To keep the error produced by the deep learning model bounded, a...

BobRossNet: Accelerating rendering by partial inpainting

Before you lies the report that resulted from the Individual Research Project of Vera Hoveling. For this project, a neural network has been developed to complete partially rendered images: BobRossNet. In this document you will find the details of the project, the architecture of BobRossNet, its training, results and a reflection on its future.