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Delft University of Technology
1.
Ament, Tjalling (author).
GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a 
► 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.…
(more)
▼ 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.
Computer Engineering
Advisors/Committee Members: Lin, Hai Xiang (mentor), te Stroet, Chris (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Machine Learning; Elastic Net; GPU; Grid Search; Feature selection
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APA (6th Edition):
Ament, T. (. (2020). GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a
Chicago Manual of Style (16th Edition):
Ament, Tjalling (author). “GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets.” 2020. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a.
MLA Handbook (7th Edition):
Ament, Tjalling (author). “GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets.” 2020. Web. 26 Jan 2021.
Vancouver:
Ament T(. GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a.
Council of Science Editors:
Ament T(. GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:f18f345f-9049-46cf-9d1e-0d3fd668474a
Delft University of Technology
2.
Guan, Siyu (author).
PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33
► This thesis project developed an alternative PM2.5 concentration prediction model and early warning system of extreme air pollution based on the long short-term memory (LSTM)…
(more)
▼ This thesis project developed an alternative PM2.5 concentration prediction model and early warning system of extreme air pollution based on the long short-term memory (LSTM) and achieved satisfying performance. To research more deeply, we divided the task into two parts. The first task was predicting the PM2.5 concentration of next 24 hours and another one was building early warning system of extreme air pollution of next 12 hours. To solve the first task, we started from the 1-hour prediction problem, that was predicting PM2.5 of next hour based on the last hours’ data. We did parameter optimization to derive the best network architecture and we got a RMSE of 19.7863. We then successfully built 24-hour prediction model that was predicting PM2.5 concentration of next 24 hours according to the optimal 1-hour prediction model. The proposed 24-hour prediction model exhibited satisfactory performance, including the 13-24 h prediction task which is predicting the mean PM2.5 concentration among next 13-24 hours (RMSE=49.41). Although we got a satisfying RMSE for the PM2.5 prediction problem, we didn’t get accurate prediction for extreme conditions and that’s why we continued to focus on the second task. We regarded the highest PM2.5 value among 12 hours as the extreme air pollution of this period and we divided the warning level into 4 parts. Then we built the early warning system based on the LSTM to predict the warning level of highest PM2.5 value of next 12 hours. As indicated by the ACC and AUC, our LSTM model achieved sound performance (ACC=86.7%, AUC=0.837). To improve the prediction performance, we focused on several model optimization techniques for the 1-hour prediction model and each technique has effectively improved the accuracy. Moreover, we combined these optimization methods together, which leaded to the lowest RMSE of 14.1937. The combined optimization method performed better than any single optimization method, which suggested that we can use some effective optimization methods together to improve the prediction accuracy of LSTM model. In addition, we also compared our model with the random forest (RF) model and the comparison result proved that LSTM network worked better for both tasks.
Applied Mathematics
Advisors/Committee Members: Lin, Hai Xiang (mentor), Cai, Juanjuan (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: LSTM; extreme air pollutation; PM2.5 prediction
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APA (6th Edition):
Guan, S. (. (2018). PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33
Chicago Manual of Style (16th Edition):
Guan, Siyu (author). “PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33.
MLA Handbook (7th Edition):
Guan, Siyu (author). “PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM.” 2018. Web. 26 Jan 2021.
Vancouver:
Guan S(. PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33.
Council of Science Editors:
Guan S(. PM2.5 concentration prediction and early warning system of extreme conditions based on the LSTM. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:9484abe6-43ba-4f68-97fe-62a3c818fa33
Delft University of Technology
3.
Xie, Yu (author).
Deep Learning Architectures for PM2.5 and Visibility Predictions.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257
► Facing the severe air pollution phenomenon in urban areas and the subsequent low visibility event in airports, it is urgent to conduct air quality and…
(more)
▼ Facing the severe air pollution phenomenon in urban areas and the subsequent low visibility event in airports, it is urgent to conduct air quality and visibility predictions to better reflect their changing trends. However, the variations of PM2.5 and visibility involve complicated physical and chemical processes, which make their accurate predictions challenging. In this thesis, methodologies to predict PM2.5, PM10, and visibility using Long Short-Term Memory Neural Networks (LSTM NN) were investigated. The first step of the proposed methodology was dataset analysis and preprocessing, which is an important step in almost all machine learning problems. Because missing data and confusing or incorrect data are common in large datasets, noise and errors were corrected and missing rates were calculated at first. Afterward, datasets were visualized to evaluate the missing phenomenon of different features. Due to the explored strong spatiotemporal correlations, for air quality features with high missing rates, linear interpolations were implemented when the missing granularity is small and k-Nearest Neighbor (kNN) imputations were used when the missing interval is large. Furthermore, the PM2.5 or PM10 prediction is usually considered as a regression task and aimed at minimizing the mean squared error (MSE) between the predicted values and measured ones. However, due to the high variability and explored ‘class-imbalance’ phenomenon of visibility data, that is, most of the data we have are related to 'normal' situations and extreme conditions are rare events, its predictions can be better dealt with as a classification problem. Because the most interesting cases to be predicted are those rare extreme events, the target was adapted to minimize the weighted cross-entropy. The second step of the proposed methodology was to configure the frameworks. For PM2.5 predictions, feature engineering was employed to the select appropriate features and some model hyperparameters were set through grid searches and coordinate descent. A coarse-to-fine sampling scheme was used to determine the weights in the loss function of visibility predictions. The third step of our research was performance evaluation. For PM2.5 predictions, the proposed spatiotemporal LSTM framework can overcome the systematic underestimation that Lotos-Euros (a chemical transport models (CTMs) based system) generally produces by analyzing their scatter plots and confusion matrices. Additionally, it performs better than an LSTM-based prediction framework (Fan J et al. (2017) [9]) that also considers spatial correlations among stations and performs a similar task in a similar region when comparing their rooted mean square errors (RMSE) and mean absolute errors (MAE). Differences between the hyperparameters of these two frameworks were analyzed. As for PM10 predictions, the training efficiency can be improved significantly by transferring knowledge from PM2.5 predictions to PM10 predictions through model fine-tuning. Compared with Lotos-Euros,…
Advisors/Committee Members: Lin, Hai Xiang (mentor), Jin, Jianbing (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: PM2.5 predictions; PM10 predictions; Visibility predictions; Deep learning; LSTM; Transfer learning
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APA (6th Edition):
Xie, Y. (. (2018). Deep Learning Architectures for PM2.5 and Visibility Predictions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257
Chicago Manual of Style (16th Edition):
Xie, Yu (author). “Deep Learning Architectures for PM2.5 and Visibility Predictions.” 2018. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257.
MLA Handbook (7th Edition):
Xie, Yu (author). “Deep Learning Architectures for PM2.5 and Visibility Predictions.” 2018. Web. 26 Jan 2021.
Vancouver:
Xie Y(. Deep Learning Architectures for PM2.5 and Visibility Predictions. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257.
Council of Science Editors:
Xie Y(. Deep Learning Architectures for PM2.5 and Visibility Predictions. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:c7ea9e3e-d6c2-426c-b4d5-8c7143052257
Delft University of Technology
4.
Huang, Jie (author).
Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf
► This thesis focuses on the construction and optimization of a prediction model for the errors resulting from a model order reduction (MOR) procedure in oil…
(more)
▼ This thesis focuses on the construction and optimization of a prediction model for the errors resulting from a model order reduction (MOR) procedure in oil reservoir simulation. MOR is a numerical technique that projects the physical based model, which is also called the high-fidelity model (HFM), into a lower dimension by using matrix decomposition, such that the computational speed can be greatly increased. The reduced order model (ROM) is also known as surrogate model. Obviously, error occurs during the projection process. We want to estimate this error and predict it through building an error model, and to fortify the surrogate model by adapting a parameter estimation. In this thesis, three statistical methods will be adapted to our problem, including least absolute shrinkage and selection operator (LASSO) and two machine learning (ML) methods: long short term memory (LSTM) and fully-connected recurrent neural network (RNN). The training data is the error of the ROM, which is defined as the difference between the ROM values and HFM values. Efforts have also been made to improve the performance of the error model, including the pre-processing of the data, and several model optimization techniques. The model order reduction method here is a non-intrusive subdomain POD-RBF algorithm, which treats subsurface oil-water flow data by adapting domain decomposition (DD), radial basis function (RBF) and proper orthogonal decomposition (POD). The high-fidelity model is generated by Matlab reservoir simulation toolbox (MRST). The error is defined as the difference between the HFM data and the ROM data. Through the comparison of several statistical models, this error can be best predicted by an optimized traditional recurrent neural network.
Applied Mathematics
Advisors/Committee Members: Lin, Hai Xiang (mentor), Heemink, Arnold (graduation committee), Cai, Juan Juan (graduation committee), Delft University of Technology (degree granting institution).
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APA (6th Edition):
Huang, J. (. (2019). Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf
Chicago Manual of Style (16th Edition):
Huang, Jie (author). “Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem.” 2019. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf.
MLA Handbook (7th Edition):
Huang, Jie (author). “Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem.” 2019. Web. 26 Jan 2021.
Vancouver:
Huang J(. Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf.
Council of Science Editors:
Huang J(. Machine Learning Based Error Modeling for Surrogate Model in Oil Reservoir Problem. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:2bc14761-3800-4315-aa7b-0455b7e393cf
Delft University of Technology
5.
Hegeman, Rick (author).
Predicting the air quality by combining model simulations with machine learning.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813
► Combating air pollution has proven to be a difficult task for countries with rapidly developing economies. Poor air quality can be hazardous to people doing…
(more)
▼ Combating air pollution has proven to be a difficult task for countries with rapidly developing economies. Poor air quality can be hazardous to people doing any outdoor activities. So being able to make accurate, short term air quality predictions can be very useful. However, making these predictions has proven to be quite difficult, since there are a lot of different physical and chemical processes involved in the emission and transport of the various aerosols that contribute to air pollution. So instead of the more traditional Chemical Transport Models (CTMs) we will be using neural networks in order to make predictions of one of these aerosols, PM2.5. In particular, we will be using a Long Short Term Memory (LSTM) network. In addition, we will include the simulations results from a CTM, LOTOS-EUROS, as input data to the LSTM network to improve the performance of the neural network. One of the main drawbacks of the LSTM approach is that whenever the PM2.5 concentration changes a lot, the predictions made by the LSTM network take some time to change as well, causing a visible time delay when looking at the measurements and predictions in the same time series plot. We will also try a simpler type of neural network, a Feedforward Neural Network (FNN) and compare its performance to that of LSTM. We found that using the simulation data does indeed improve the LSTM network. Not only in terms of the loss function used by the neural network and, but in particular in the amount gross overestimations by the network, which we use to quantify the LSTM time delay problem. We also found that FNN outperforms the LSTM approach, in particular on samples of high PM2.5 concentrations, which we argue is primarily caused by a low amount of samples in our dataset.
Advisors/Committee Members: Lin, Hai Xiang (mentor), Heemink, Arnold (mentor), van Gijzen, Martin (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: PM2.5 predictions; LSTM; Deep Learning
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APA (6th Edition):
Hegeman, R. (. (2020). Predicting the air quality by combining model simulations with machine learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813
Chicago Manual of Style (16th Edition):
Hegeman, Rick (author). “Predicting the air quality by combining model simulations with machine learning.” 2020. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813.
MLA Handbook (7th Edition):
Hegeman, Rick (author). “Predicting the air quality by combining model simulations with machine learning.” 2020. Web. 26 Jan 2021.
Vancouver:
Hegeman R(. Predicting the air quality by combining model simulations with machine learning. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813.
Council of Science Editors:
Hegeman R(. Predicting the air quality by combining model simulations with machine learning. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:0eefc9a3-5462-4eee-8907-955ae4b95813
Delft University of Technology
6.
Dolas, Sagar (author).
High Performance Data Traversal: Cache Aware Computing With Space Filling Curve.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e
► "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…
(more)
▼ "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.
Advisors/Committee Members: Vuik, Kees (mentor), Möller, Matthias (mentor), Lin, Hai Xiang (mentor), Galavi, V (mentor), Delft University of Technology (degree granting institution).
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APA ·
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APA (6th Edition):
Dolas, S. (. (2017). High Performance Data Traversal: Cache Aware Computing With Space Filling Curve. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e
Chicago Manual of Style (16th Edition):
Dolas, Sagar (author). “High Performance Data Traversal: Cache Aware Computing With Space Filling Curve.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e.
MLA Handbook (7th Edition):
Dolas, Sagar (author). “High Performance Data Traversal: Cache Aware Computing With Space Filling Curve.” 2017. Web. 26 Jan 2021.
Vancouver:
Dolas S(. High Performance Data Traversal: Cache Aware Computing With Space Filling Curve. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e.
Council of Science Editors:
Dolas S(. High Performance Data Traversal: Cache Aware Computing With Space Filling Curve. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:5503075e-fe4c-4a87-85e5-ec6c6d5ec53e
Delft University of Technology
7.
Geçmen, Dilan (author).
Deep Learning Techniques for Low-Field MRI.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911
► Delft University of Technology (TU Delft), Leiden University Medical Center (LUMC), Pennsylvania State University (PSU) and Mbarara University of Science and Technology (MUST) have an…
(more)
▼ Delft University of Technology (TU Delft), Leiden University Medical Center (LUMC), Pennsylvania State University (PSU) and Mbarara University of Science and Technology (MUST) have an ongoing collaboration to create an affordable, portable and simplified version of the magnetic resonance imaging (MRI) scan for the CURE children’s hospital to diagnose children with hydrocephalus (water on the brain). As opposed to the conventional MRI scan, the low-field MRI prototype uses permanent magnets to create a magnetic field in the order of Milliteslas (mT). A downside of the low-field MRI application is the difficulty with spatial encoding due to small variations in the strength of magnetic field. This is a major problem for image reconstruction. The purpose of this research was to implement a deep learning (DL) network to overcome two of the major bottlenecks in image reconstruction for low-field MRI. These are the lack of real measured data for DL purposes, and the signal model associated with the low-field MRI. For DL purposes we generated synthetic data and acquired measured data. Each dataset consists of samples and each sample consist of an image and the corresponding signal. Due to technical limitations the measured dataset is small, 53 samples. To partially circumvent the problem, the data set was augmented to a total of 1908 samples. In addition, we used Transfer learning, which is a powerful method that applies knowledge gained from one problem to a different but related problem. We present three image reconstruction techniques, Model I, II, and III, based on convolutional and feedforward neural networks, which take MR signal data as input and directly and quickly outputs an image. We demonstrated that DL generates high quality images using synthetic data. In addition, we showed that Model III needs less training to reconstructs good quality images compared to Models I and III, respectively. Finally, Models I and III were unsuccessfully applied to real measured data. However, this study shows that neural networks are able to find a mapping between signal and image, therefore this idea can be extended to work on real measured data.
Applied Mathematics
Advisors/Committee Members: van Gijzen, Martin (mentor), de Leeuw den Bouter, Merel (graduation committee), Remis, Rob (graduation committee), Lin, Hai Xiang (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Deep Learning; Magnetic Resonance Imaging
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APA ·
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APA (6th Edition):
Geçmen, D. (. (2020). Deep Learning Techniques for Low-Field MRI. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911
Chicago Manual of Style (16th Edition):
Geçmen, Dilan (author). “Deep Learning Techniques for Low-Field MRI.” 2020. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911.
MLA Handbook (7th Edition):
Geçmen, Dilan (author). “Deep Learning Techniques for Low-Field MRI.” 2020. Web. 26 Jan 2021.
Vancouver:
Geçmen D(. Deep Learning Techniques for Low-Field MRI. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911.
Council of Science Editors:
Geçmen D(. Deep Learning Techniques for Low-Field MRI. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:ce264a44-ddd5-45c5-96d0-c82aac0e4911
Delft University of Technology
8.
van Nieuwpoort, Ruben (author).
Solving Poisson's equation with dataflow computing.
Degree: 2017, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3
► The finite element method (FEM) is an ubiquitous method for the analysis of boundary value problems. Specifically, it can be used to find approximations to…
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▼ The finite element method (FEM) is an ubiquitous method for the analysis of boundary value problems. Specifically, it can be used to find approximations to solutions of boundary value problems on a specific domain...
Advisors/Committee Members: Gaydadjiev, Georgi (mentor), Möller, Matthias (mentor), Vuik, Kees (graduation committee), Lin, Hai Xiang (graduation committee), van Genderen, Arjan (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Finite element analysis; isogeometric analysis; weighted quadrature; Matrix-free solution techniques; Dataflow computing; mathematical theory; MAX5 hardware resources
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APA (6th Edition):
van Nieuwpoort, R. (. (2017). Solving Poisson's equation with dataflow computing. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3
Chicago Manual of Style (16th Edition):
van Nieuwpoort, Ruben (author). “Solving Poisson's equation with dataflow computing.” 2017. Masters Thesis, Delft University of Technology. Accessed January 26, 2021.
http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3.
MLA Handbook (7th Edition):
van Nieuwpoort, Ruben (author). “Solving Poisson's equation with dataflow computing.” 2017. Web. 26 Jan 2021.
Vancouver:
van Nieuwpoort R(. Solving Poisson's equation with dataflow computing. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Jan 26].
Available from: http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3.
Council of Science Editors:
van Nieuwpoort R(. Solving Poisson's equation with dataflow computing. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:c5dfd1d4-6494-47e9-90d9-486d2a7b26b3
. 
Open Access Theses and Dissertations