In dit onderzoek hebben we een nieuwe dienstregeling voor tramlijn 19 opgesteld. Deze tramlijn rijdt nu tussen Delft en Leidschenhage. In 2020 zal het traject worden uitgebreid met haltes op de campus van de TU Delft. In dit onderzoek hebben we twee dienstregelingen opgesteld: één voor in de spitsuren, en één voor in de daluren. Er zijn een aantal voorwaarden meegenomen in het onderzoek. Zo moet de dienstregeling aansluiten op de collegetijden van de studenten die gebruik maken van tram 19. Ook moeten we de voorwaarden die we in de huidige dienstregeling vinden, meenemen in het nieuwe traject. Denk bijvoorbeeld aan de frequentie waarin trams rijden, of wanneer de dienstregeling start op een dag. Daarnaast hebben we onderzocht hoe het beste kan worden overgegaan van een spitsuur dienstregeling naar een met daluren. Dit alles is gedaan met behulp van max-plus algebra, waarbij het nemen van een maximum en optellen, de conventionele operaties van optellen en vermenigvuldigen vervangen. Het power algoritme helpt ons bij het kiezen van de juiste vertrektijden, zodat we een constant vertrekschema krijgen. Ook maken we gebruik van Petrinetten omhet gehele netwerk temodelleren. In dit onderzoek wordt steeds eerst gekeken naar een kleiner of versimpeld tramnetwerk. Hierna passen we de theorie toe op het toekomstige traject. Op het einde hebben we een dienstregeling kunnen opstellen voor tramlijn 19. In deze dienstregeling vindt afwisseling plaats tussen spits-en daluren, met elk een andere dienstregeling en hoeveelheid trams. Hierbij is rekening gehouden met de voorwaarden die wij voor ons model hebben opgesteld.

Chemical transport models (CTMs) are used to improve our understanding of the complex processes influencing atmospheric composition, as well as provide operational air quality forecasts and model potential future air quality scenarios. Numerical tracers in CTMs track the concentration of chemical species, while operators simulate various physical processes such as advection. One such CTM, LOTOS-EUROS, uses a volatility basis set (VBS) approach to represent the formation of organic aerosol (OA) in the atmosphere, which contributes to the concentration of total particulate matter. The added dimensionality of the VBS tracers in LOTOS-EUROS slowed down computation of the advection operator by a factor of two, limiting their representation in operational forecasts. To keep the detailed process representation of OA formation, while reducing the computational costs, we develop an unsupervised machine learning method to compress the VBS tracers to a set of superspecies for use in advection, and subsequently decompress superspecies back to the tracer space for OA-relevant calculations. The focus of this machine learning method is physical interpretability, allowing for operators to resolve equations using the superspecies. This method conserves mass to machine precision and retains important information like phase (gas or aerosol) on compression. This data-driven approach reduces the dimensionality of the system more than a second proposed approach based on partitioning theory. The ML superspecies approach was integrated into LOTOS-EUROS for online calculations, showing numerical stability over a model simulation time of two weeks under various conditions. With the superspecies, the computation time for advection is reduced by 56% to 66% of the time for advection of the VBS tracers. The results of this approach show potential for use in accelerating air quality operational forecasts, as well as pathways forward for integration of ML box models of atmospheric chemistry into CTMs.

Neural network is an active research field which involves many different (unsolved) issues, for example, different types of configuration of the network architectures, training strategies, etc. Amongst these active issues, the choice of loss (or cost) functions plays an important role in how a neural network model is to be optimized (trained) and how the model will perform after the training. Given the choice of measurement criteria, loss functions measure how far an estimated output is from its true value. And the measurement criteria can change depending on the task in hand and the goal to be met. The objective of this project is to understand the role of different loss functions and to evaluate the dependence of the performance on the loss functions using the language prediction problem.

To optimize the exploitation of oil and gas reservoirs both on- and offshore, Biodentfiy has developed a method to predict prospectivity of hydrocarbons before drilling. This method uses microbiological DNA analysis of shallow soil or seabed samples to detect vertical upward microseepage from hydrocarbon accumulations, which change the composition of microbes at the surface. Microbiological DNA analysis of shallow soil or seabed samples results in a high-dimensional dataset, which is interpreted using machine learning. Using the machine learning method Elastic Net, features (microbes) are selected from an existing DNA database to classify new shallow soil or seabed samples. Multiple models, each with a different combination of externally set parameters (called hyperparameters), are trained to improve accuracy, essentially creating a grid of models. The aim of this thesis is to investigate if it is possible to accelerate feature selection on high-dimensional datasets by implementing a parallel design on a GPU to train this grid of models, and to investigate the performance of this GPU implementation. Inspired by an implementation called Shotgun, which is able to improve performance by exploiting parallelism across features when training a single model on a CPU, an implementation, named GPU Shotgun (GPU-SG) was devised, which could exploit parallelism across samples, features, and multiple models in the grid (of combinations of hyperparameters). Depending on the size of the grid and the hardware, using GPU-SG, a speedup of between 0.2 and 5.26 can be reached for sparse datasets (a datasets with lots of 0 values) when compared to standard CPU implementations. When considering dense datasets (a dataset with few 0 values), using GPU-SG, a speedup of between 0.5 and 10 can be achieved. The amount of memory available to store a dataset is lower for GPU's than for a CPU, and currently the design is limited by this, because the design does not allow a dataset that is larger than the memory available. GPU-SG can be used to design improved implementations, which reduce the time when the GPU or CPU is idle to improve performance.

In this project, we develop a mathematical framework that automates AC power flow simulations by solving both converging and diverging power flow problems. On the basis of the mathematical framework, we develop an algorithm that can be used to perform year-round AC power flow simulations with little manual intervention. We demonstrate the applicability of the algorithm by implementing it on artificial test networks, and two real-world transmission networks - the Dutch transmission network and a sub-European transmission network.

When building a convolutional neural network, many design choices have to be made. In the case of Deepfake detection, there is no readily implementable recipe that guides these choices. This research aims to work towards understanding the effects of design choices in the case of Deepfake detection, using the Python library Keras and publicly available datasets. The choices analysed are dataset composition, preprocessing, dropout rate, batch size, network architecture, and specific dataset. We also analyse the difference between training a network on original images and DCT-residuals of images. Lastly, we analyse the networks' generalisation and robustness capabilities. The goal of these experiments is to work towards a readily implementable recipe for Deepfake detection algorithms. Furthermore, this research provides an overview of image manipulation algorithms, an overview of recent research into convolutional networks, and an extensive overview of the Deepfake detection research field. To analyse dataset composition, we used different subsets of FaceForensics++, with different numbers of frames per video. We trained a shallow network, containing only four convolutional layers, on all three datasets. The dataset with one frame per video was the only one that did not result in immediate overfitting, although it contained less than two thousand frames in total. We continued with the small dataset and tested different preprocessing settings and dropout rates on our shallow network. We found that preprocessing and dropout were not able to increase the maximum achievable accuracy, although they were able to curtail overfitting. It is possible that accuracy did not increase due to the high variety of artefacts in FaceForensics++. Batch size also does not have any effect on the maximum achievable accuracy. However, the runtime required for training a network increases considerably as the batch size decreases. We tested DenseNet-121, Inception-v3, ResNet-152, VGG16, VGG19, Xception, and our shallow network on three different datasets: FaceForensics++, Celeb-df, and DeeperForensics-1.0. The goal of this experiment was to find what type of network is most suited for detecting Deepfakes with publicly available datasets of small size but high variation. We also wanted to see if there were differences in performance achieved on the different datasets. Although all networks except the shallow one were pretrained on ImageNet, three of the different networks tested immediately overfitted on all three datasets used: DenseNet-121, Inception-v3, and XceptionNet. The other networks encountered most difficulties with Celeb-df, on which none of the networks managed to reach 70% accuracy before overfitting. The easiest network to train on was DeeperForensics-1.0, on which our shallow network achieved 92.5% accuracy. However, when testing the networks' robustness, none of the networks trained on DeeperForensics-1.0 reached an accuracy higher than random on Celeb-df or FaceForensics-1.0. Shallow networks might be better suited for Deepfake detection on our small dataset. The shallow model and VGG16 achieved the highest accuracies. VGG19's performance is close to VGG16's. However, ResNet-152 is the deepest network used in this research and performs better than the shallower DenseNet, Inception-v3, and Xception. Lastly, training our networks on DCT-residuals was supposed to help our network focus on statistical image content rather than semantical image content. However, performance on DCT-residuals was at best similar to performance on original images. Our suggestions for continuation of this research are to experiment with different input sizes and other types of residual filters, collect larger (and higher quality) datasets with high variety, and to use interframe detection.

"What Mathematics is to Physics, Data traversal is to High-performance computing." The world of Computational science has witnessed an exponential expansion of sophisticated numerical algorithms in the last few decades mainly to understand minute details and solve complex physical problems. It has established itself as the third pillar of science after theory and experimentation and has managed to gain immense popularity as a mainstream research work among academicians and scientists working in entirely different fields. The Computational Sciences has brought together Mathematicians and Computer Scientists to work in close collaboration on the variety of interdisciplinary research problems. The principal challenge to achieve high performance for computational researchers in near about every front is data traversal, data placement, and memory access pattern which mostly influences floating point performance and energy efficiency. The Data traversal is the soul of high-performance computing. Indeed it is the backbone; the way data travels to the CPU from main memory mainly influences the performance of particular computational kernel on specific machine architecture. The majority of modern computing devices are designed to deliver high performance if data traversal can utilize maximum bandwidth to main memory (DRAM) and make efficient use of hierarchical memory structure. Thus, a hardware optimal data access pattern should be designed to take advantage of the underlying hardware to scale and achieve performance, and that forms the central theme of this work. The more important point here is, expensive hardware or massive computational infrastructure does not naturally invoke high-performance computing but implementation of hardware auxiliary mathematical ideas, cache efficient data traversal strategies, sensible use of parallel programming paradigms and energy aware management of computational resources on machines ranging from very grass-root level primary NUMA system to entire million core server stack does. In this master's thesis, we will first focus on investigating the impact of data traversal patterns on the performance of several micro-benchmarks on Non-Uniform Memory Access machine, and in the second part, we will implement Morton-order Space Filling Curve to improve cache utilization for two numerical methods and analyze performance impact.