Searched for: subject%3A%22graph%255C+neural%255C+network%22
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Xu, J. (author)
Deep Neural Networks (DNNs) have found extensive applications across diverse fields, such as image classification, speech recognition, and natural language processing. However, their susceptibility to various adversarial attacks, notably the backdoor attack, has repeatedly been demonstrated in recent years. <br/>The backdoor attack aims to...
doctoral thesis 2025
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Corvi, Giovanni (author)
The rapidly growing volume of parcel shipments is straining transportation and logistics sectors, highlighting the need for innovative solutions to optimize packing and loading processes. The online bin packing problem (BPP), an NP-hard computational problem, finds practical applications in numerous sectors, including modern packaging and...
master thesis 2024
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Neagu, Alex (author)
As the power system grows more complex and active, equivalent models have become a solution for modelling parts of the network that have limited observability or are confidential or too complex to simulate otherwise. In the past decade, this topic has also made its way to distribution networks because of its transition towards an active network,...
master thesis 2024
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Nelemans, Peter (author)
Fully distributed hydrological models take into account the spatial variability of a catchment, and allow for assessing its hydrological response at virtually any location. However, these models can be time-consuming when it comes to model runtime and calibration, especially for large-scale catchments. Meanwhile, deep learning models have shown...
master thesis 2024
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Sebus, Siert (author)
The Deep Neural Network (DNN) has become a widely popular machine learning architecture thanks to its ability to learn complex behaviors from data. Standard learning strategies for DNNs however rely on the availability of large, labeled datasets. Self-Supervised Learning (SSL) is a style of learning that allows models to also use unlabeled data...
master thesis 2024
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McDonald, Tom (author), Tsay, Calvin (author), Schweidtmann, A.M. (author), Yorke-Smith, N. (author)
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are mostly limited to MLPs. Graph neural networks (GNNs) can learn from non-euclidean data...
journal article 2024
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Storm, J. (author), Rocha, I.B.C.M. (author), van der Meer, F.P. (author)
Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every...
journal article 2024
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Tsehaie, Nahom (author)
Inland waterway shipping, marked by its unpredictable and variable nature, plays a crucial role in transportation. This research's objective is to address these inconsistencies by constructing a robust scheduling model tailored to waterway systems' specific needs and challenges. The model is enhanced with predictive analytics and optimisation...
master thesis 2023
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Veeger, Lucas (author)
Reducing cost and improving computability of reservoir simulation is an important goal in the process of enabling CCS (Carbon Capture \&amp; 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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Yang, Yufeng (author)
Event-based cameras promise new opportunities for smart vision systems deployed at the edge. Contrary to their conventional frame-based counterparts, event-based cameras generate temporal light intensity changes as events on a per-pixel basis, enabling ultra-low latency with microsecond-scale temporal resolution, low power consumption at...
master thesis 2023
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VASILEIOU, ANTONIOS (author)
Graph data is widely used in various applications, driving the rapid development of graph-based machine learning methods. However, traditional algorithms tailored for graphs have constraints in capturing intricate node relationships and higher-order patterns. Recent insights from prior research have shed light on comparing different graph neural...
master thesis 2023
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van Linn, Joseph (author)
Snow is a natural hazard to human life and infrastructure. This motivates current research efforts to understand the granular material. The material point method models snow as a continuum. Application length scales range from the microstructural level to full scale avalanches. This conventional numerical method relies on solely spatially local...
master thesis 2023
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Timp, Lennard (author)
Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal...
bachelor thesis 2023
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Martens, Bruno (author)
Critical to the safe application of autonomous vehicles is the ability to accurately predict the future motion of agents surrounding the vehicle. This is especially important - and challenging - in urban traffic, where vehicles share the road with Vulnerable Road Users (VRUs) such as pedestrians and cyclists. However, the majority of the...
master thesis 2023
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Cong, Shijie (author)
Autonomous robots have been widely applied to search and rescue missions for information gathering about target locations. This process needs to be continuously replanned based on new observations in the environment. For dynamic targets, the robot needs to not only discover them but also keep tracking their positions. Previous works focus on...
master thesis 2023
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Kaniewski, Tadeusz (author)
The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...
master thesis 2023
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Croft, Maxime (author)
This paper presents a novel approach to regional forecasting of SARS-Cov-2 infections one week ahead, which involves developing a municipality level COVID-19 dataset of the Netherlands and using a spatio-temporal graph neural network (GNN) to predict the number of infections. The developed model captures the spread of infectious diseases within...
master thesis 2023
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Olatunji, Iyiola E (author), Rathee, Mandeep (author), Funke, Thorben (author), Khosla, M. (author)
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on...
conference paper 2023
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Rahmani, S. (author), Baghbani, Asiye (author), Bouguila, Nizar (author), Patterson, Zachary (author)
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no...
journal article 2023
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Madadi, B. (author), Correia, Gonçalo (author)
This study proposes a hybrid deep-learning-metaheuristic framework with a bi-level architecture for road network design problems (NDPs). We train a graph neural network (GNN) to approximate the solution of the user equilibrium (UE) traffic assignment problem and use inferences made by the trained model to calculate fitness function...
journal article 2023
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