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A machine learning model for the estimation of hourly non-tidal water levels in the Dutch coastal zone: Based on satellite altimetry observations and pressure and wind fields from ERA5

Schijvenaars, Sofie (author)

Globally, coastal communities face increasing risks from climate-related hazards such as flooding, shoreline erosion, and salt intrusion. These hazards pose threats to both people and their environment, with extreme sea level events increasing these risks. Satellite altimetry allows for global observation of the sea level, reaching remote...

master thesis 2024

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Application of machine learning in the structural design process of bascule bridges: Using an artificial neural network to generate structural bascule bridge designs in Grasshopper

Vos, Geert (author)

This research has aimed to investigate the possibility of applying a neural network algorithm into the structural design process of bascule bridge leaves, by creating a workflow in Grasshopper. The demand for this tool, originates from the fact that the current design process is experienced as linear and slow, and does not fit the dynamic design...

master thesis 2024

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CCS Reservoir Simulation using Graph Neural Networks: Building ML solutions for efficient CO2 subsurface modelling

Veeger, Lucas (author)

Reducing cost and improving computability of reservoir simulation is an important goal in the process of enabling CCS (Carbon Capture \& Storage) as a large-scale technology for mitigating CO2 emissions. In terms of computation time data-driven approaches have potential to outweigh the performance of numerical reservoir simulators, learning...

master thesis 2023

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Microtubule Detection and Instance Segmentation using Deep Convolutional Neural Networks

Holtgrefe, Tim (author)

Microtubules are long cylindrical polymers, assembled from tubulin proteins. Microtubule ends can be visualized using fluorescence and confocal microscopy. This allows for the study of microtubule dynamics. However, the manual annotation of microtubules is laborious, which is why automated tracking methods are used. In this project we have...

bachelor thesis 2023

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Neural-Network Based Thermal Modeling of Small Satellites: A First-Principles Approach

Bhat, Ullas (author)

The use of small satellites, enabled by the standardization of the CubeSat specifications and miniaturization in electronics, has seen a rapid increase in the past decades. The low-cost and short development time of these satellites has made them an attractive option for both commercial and academic applications, making space exploration more...

master thesis 2023

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Machine learning for post-storm profile predictions: Using XBeach and convolutional neural network structure U-Net to predict 1D dune erosion profile shapes at the Holland Coast

van Asselt, Koen (author)

To reduce computational efforts, surrogate models have been developed for dune erosion prediction. Current surrogate models can describe the relationship between the XBeach input and output (Athanasiou, 2022) and provides a prediction of a morphological indicator based on a parameterized input (profile shape parameters and hydrodynamics). In...

master thesis 2023

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Simulating Human Motor Learning: An Old Solution in New Environments

Gu, Zhengping (author)

Feedback error learning (FEL) is a classical computational model that describes human motor learning. It consists of forward and inverse models representing internal dynamics and environmental disturbances. Such models can be used as controllers that represent the function of the motor cortex. On top of FEL, a model has been built with jointly...

master thesis 2023

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Estimating Wind Fields Using Physically Inspired Neural Networks With Aircraft Surveillance Data

Malfliet, Jari (author)

Wind estimation plays an important role in many aspects of our world, both for nowcasting and forecasting. This is also true for aircraft flight routes; where wind field knowledge can contribute to flight planning and safety. Aircraft can relay flight information back to ground-based receivers, from which local wind measurements can be derived....

master thesis 2023

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Gradient descent-based freeform optics design for illumination using algorithmic differentiable non-sequential ray tracing

de Koning, B. (author), Heemels, A.N.M. (author), Adam, A.J.L. (author), Möller, M. (author)

Algorithmic differentiable ray tracing is a new paradigm that allows one to solve the forward problem of how light propagates through an optical system while obtaining gradients of the simulation results with respect to parameters specifying the optical system. Specifically, the use of algorithmically differentiable non-sequential ray tracing...

journal article 2023

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BoundED: Neural boundary and edge detection in 3D point clouds via local neighborhood statistics

Bode, Lukas (author), Weinmann, M. (author), Klein, Reinhard (author)

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand. Existing approaches are still not able to produce high-quality results consistently while being fast enough to be deployed in scenarios...

journal article 2023

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Benchmarking and Algorithm Optimization for SENeCA: A RISC-V-based Neuromorphic Processor

Kevin Shidqi, Kevin (author)

With recent breakthroughs in AI (Artificial Intelligence) technology, the impact of AI on society can be felt in various fields. The market for AI software, for example, reached a valuation of \$62 billion in 2022. A growing number of new computer architectures specialized in running these AI software were also developed. At first they were run...

master thesis 2022

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Predicting the optimal CFL number for pseudo time-stepping: with machine learning in the COMSOL CFD module

Zandbergen, Anouk (author)

Commercial finite element software like COMSOL is build to be user-friendly. For example, the user does not have to find weak formulations, or discretise the partial differential equations by hand. One of the more difficult parts of using finite element software is deciding which solver method and solver parameters to choose, as the performance...

master thesis 2022

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Guided Metamorphic Transformations for Testing the Robustness of Trained Code2Vec Models

Marang, Ruben (author)

Machine learning models are increasingly being used within software engineering for their predictions. Research shows that these models’ performance is increasing with new research. This thesis focuses on models for method name prediction, for which the goal is to have a model that can accurately predict method names. With this thesis, we could...

master thesis 2022

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Neural networks-based algorithms for option pricing

Rou, Jasper (author)

In this research, we consider neural network-algorithms for option pricing. We use the Black-Scholes model and the lifted Heston model. We derive the option pricing partial differential equation (PDE), which we solve with a neural network, and the conditional characteristic function of the stock price which leads to the option price with the COS...

master thesis 2022

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Can PredNet predict time-series to improve the generalization process?

Jousma, Sjoerd (author)

This thesis is inspired by Active Inference to contribute to its improvement in the Robotics work field. However, the results and applications of this thesis are useful in a broader perspective, namely in any field that makes use of derivatives and the forecasting of a time-series signal. The goal of this study is to determine a new approach for...

master thesis 2022

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Neural networks for Gamma cameras

de Hartog, André (author)

In this research the effectiveness of analytical neural networks compared to the maximum likelihood method on the prediction of spatial and DOI positioning of a Gamma detector with a NaI(Tl) scintillator of size 590mm x 470mm x 40mm (x,y,z), with a glass lightguide of size 620mm x 500mm x 4mm and a PMT area of 620mm x 500mm x 40mm with 2-inch...

bachelor thesis 2022

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Empirical Evaluation of the Performance of CEVAE under Misspecification of the Latent Dimensionality

Barták, Patrik (author)

Causal machine learning deals with the inference of causal relationships between variables in observational datasets. <br/>For certain datasets, it is correct to assume a causal graph where information about unobserved confounders can only be obtained through noisy proxies, and CEVAE aims to address this case. <br/>The number of dimensions of...

bachelor thesis 2022

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Impact of seismic wave length to detect high-magnitude earthquakes via deep learning

Georgiev, Gancho (author)

Earthquakes are one of the most destructive natural phenomena, both in terms of human lives, and property damage. Although they are treated as a random phenomenon, the ability to predict them, even few seconds before they occur, could be of great benefit to society. Lots of research has been done on this topic but without any significant results...

bachelor thesis 2022

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Analysing machine learning models for reducing the workload of security analysts in a SOC

Veraart, Maartje (author)

The rise of alarming cyber breaches and cyber security attacks is causing the world to consider the security of our cyber space. A Security Operations Center (SOC) is a center where the security of a company is monitored to prevent cyber breaches. Security analysts in the SOC examine alerts that come from different devices and analyse what is...

master thesis 2022

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Vehicle Sideslip Angle Estimation using a Hybrid Approach: Vehicle Sideslip Angle Estimation using the combination of Uncertainty Neural Networks and Kalman Filter variations

de Mol, Dennis (author)

Accurate and robust vehicle state estimation is important for proper operation of vehicle control systems. For lateral stability control accurate estimation of the Vehicle Sideslip Angle (VSA) is of utmost importance. This thesis aims to develop an accurate and highly robust model to estimate the VSA. Common vehicle sensors such as the Inertial...

master thesis 2022