Laminar-turbulent transition has a strong impact on aerodynamic performance in many practical applications. Hence, there is a practical need for developing reliable and efficient transition prediction models, which form a critical element of the CFD process for aerospace vehicles across multiple flow regimes. This dissertation explores machine learning approaches to develop transition models using data from computations based on linear stability theory. Such data provide strong correlation with the underlying physics governed by linearized disturbance equations. In the proposed transition model, a convolutional neural network-based model encodes information from boundary layer profiles into integral quantities. Such automated feature extraction capability enables generalization of the proposed model to multiple instability mechanisms, even for those where physically defined shape factor parameters cannot be defined/determined in a consistent manner. Furthermore, sequence-to-sequence mapping is used to predict the transition location based on the mean boundary layer profiles. Such an end-to-end transition model provides a significantly simplified workflow. Although the proposed model has been analyzed for two-dimensional boundary layer flows, the embedded feature extraction capability enables their generalization to other flows as well. Neural network-based nonlinear functional approximation has also been presented in the context of transport equation-based closure models. Such models have been examined for their computational complexity and invariance properties based on the transport equation of a general scalar quantity. The data-driven approaches explored here demonstrate the potential for improved transition prediction models. / Doctor of Philosophy / Surface skin friction and aerodynamic heating caused by the flow over a body significantly increases due to the transition from laminar to turbulent flow. Hence, efficient and reliable prediction of transition onset location is a critical component of simulating fluid flows in engineering applications. Currently available transition prediction tools do not provide a good balance between computational efficiency and accuracy. This dissertation explores machine learning approach to develop efficient and reliable models for predicting transition in a significantly simplified manner. Convolutional neural network is used to extract features from the state of boundary layer flow at each location along the body. These extracted features are then processed sequentially using recurrent neural network to predict the amplification of instabilities in the flow, which is directly correlated to the onset of transition. Such an automated nature of feature extraction enables the generalization of this model to multiple transition mechanisms associated with different flow conditions and geometries. Furthermore, an end-to-end mapping from flow data to transition prediction requires no user expertise in stability theory and provides a significantly simplified workflow as compared to traditional stability-based computations. Another category of neural network-based models (known as neural operators) is also examined which can learn functional mapping from input variable field to output quantities. Such models can learn directly from data for complex set of problems, without the knowledge of underlying governing equations. Such attribute can be leveraged to develop a transition prediction model which can be integrated seamlessly in flow solvers. While further development is needed, such data-driven models demonstrate the potential for improved transition prediction models.

The deployment of distributed photovoltaic (PV) is accelerating worldwide. Understanding when and where PV systems will generate electricity is valuable as it affects the power balance in the grids. One way of obtaining this information is simulating the PV power production of systems detected in Remotely Sensed Data (RSD). The use of aerial imagery and machine learning models has proven effective for identifying solar energy facilities. In a Swedish research project, a Convolutional Neural Network (CNN) could identify 95% of all PV systems within a municipality. Furthermore, using Light Detection and Ranging (LiDAR) data, the orientation and area of detected PV systems can be estimated. Combining this information, with local weather and irradiance data, the historic PV power generation can be simulated. The purpose of this study is to adapt and validate a model for simulating historic decentralized PV electricity generation, based on an optimization tool developed by Becquerel Sweden, and further develop the model to simulate aggregated electricity generation on a municipality level where the individual orientation of each PV system is taken into account. The model has a temporal resolution of 1 hour and a spatial resolution of 2.5×2.5 km.  A regression analysis demonstrated that the simulated generation corresponds well to the measured generation of 7 reference systems, with coefficients of determination ranging from 0.69–0.84. However, the model tends to overestimate the production compared to the measured values, with a higher total simulated production and positive mean bias errors. The correlation of the measured and generated PV power was similar, when simulating using orientations provided by the reference facility owners and LiDAR approximated orientations. Generic module parameters and an average DC/AC ratio were derived in this study, enabling simulation on a municipal level. Due to available RSD, Knivsta Municipality was the object for this study. The aggregated PV electricity generation was simulated for 2022, using both an estimation of optimal conditions and an estimation of real conditions. This was compared to the assumption that all installed AC capacity in the municipality is fed to the grid. The results show that during the highest production hour, the electricity generation resulting from estimated optimal conditions, exceeds the total installed AC capacity, while the simulation using approximated real conditions never reach the total installed AC capacity. However, the average hourly production for both scenarios, never exceeds 45% of the total installed AC capacity.

One of every eight women will in their lifetime suffer from breast cancer, making it the most common type of cancer for women. A successful treatment is very much dependent on identifying metastatic tissue which is cancer found beyond the initial tumour. Using deep learning within biomedical analysis has become an effective approach. However, its success is very dependent on large datasets. Data augmentation is a way to enhance datasets without requiring more annotated data. One way of doing this is using the cutout method which masks parts of an input image. Our research focused on investigating how the cutout method could improve the performance of Convolutional Neural Networks for classifying metastatic tissue on the Patch Camelyon dataset. Our research showed that improvements in performance can be achieved by using the cutout method. Further, our research suggests that using a non label- preserving version of cutout is better than a label- preserving version. The most improvement in accuracy was seen when we used a randomly sized cutout mask. The experiment resulted in an increase in accuracy by 3.6%, from the baseline of 82,3% to 85.9%. The cutout method was also compared- and used in conjunction with other well- established data augmentation techniques. Our conclusion is that cutout can be a competitive form of data augmentation that can be used both with and without other data augmentation techniques. / Var åttonde kvinna drabbas under sin livstid av bröstcancer. Detta gör det till den vanligaste formen av cancer för kvinnor. En framgångsrik behandling är beroende av att kunna identifiera metastatisk vävnad, vilket är cancer som spridit sig bortom den ursprungliga tumören. Att använda djupinlärning inom biomedicinsk analys har blivit en effektiv metod. Dock är dess framgång väldigt beroende av stora datamängder. Dataförstärkning är olika sätt att förbättra en mängd data som inte innebär att addera ytterligare annoterad data. Ett sätt att göra detta är genom den en metod som kallas Cutout som maskar en del av en bild. Vår studie undersöker hur Cutout påverkar resultatet när Convolutional Neural Networks klassificerar huruvida bilder från datasetet Patch Camelyon innehåler metastaser eller inte. Vår studie visar att användandet av Cutout kan innebära förbättringar i resultatet. Dessutom tyder vår studie på att resultatet förbättras än mer om även delen av bilden som kan innehålla metastaser kan maskas ut. Den största förbättringen i resultatet var när maskningen var av varierande storlek från bild till bild. Resultatet förbättrades från 82.3% korrekta klassifikationer utan någon dataförstärkning till 85.9% med den bästa versionen av Cutout. Cutout jämfördes också, och användas tillsammans med, andra väletablerade dataförstärkningsmetoder. Vår slutsats är att Cutout är en dataförstärkningsmetod med potentital att vara användbar såväl med som utan andra dataförstärkningsmetoder.

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Nowadays inspections of civil engineering structures are performed manually at close range to be able to assess damages. This requires specialized equipment that tends to be expensive and to produce closure of the bridge. Furthermore, manual inspections are time-consuming and can often be a source or risk for the inspectors. Moreover, manual inspections are subjective and highly dependent on the state of mind of the inspector which reduces the accuracy of this kind of inspections. Image-based inspections using cameras or unmanned aerial vehicles (UAV) combined with image processing have been used to overcome the challenges of traditional manual inspections. This type of inspection has also been studied with the use of machine learning algorithms to improve the detection of damages, in particular cracks. This master’s thesis presents an approach that combines different aspects of the inspection, from the data acquisition, through the crack detection to the quantification of essential parameters. To do this, both digital cameras and a UAV have been used for data acquisition. A convolutional neural network (CNN) for the identification of cracks is used and subsequently, different quantification methods are explored to determine the width and length of the cracks. The results are compared with control measures to determine the accuracy of the method. The results present low to no false negatives when using the CNN to identify cracks. The quantification of the identified cracks is performed obtaining the highest accuracy estimation for 0.2mm cracks.

Bladder cancer (BCa) is the fourth most commonly diagnosed cancers in men and the eighth most common in women. It is an abnormal growth of tissues which develops in the bladder lining. Histological analysis of bladder tissue facilities diagnosis as well as it serves as an important tool for research. To bet- ter understand the molecular profile of bladder cancer and to detect predictive and prognostic features, microscopy methods, such as immunofluorescence (IF), are used to investigate the characteristics of bladder cancer tissue. For this project, a new method is proposed to segment cancer epithelial us- ing nuclei morphology captured with IF staining. The method is implemented using deep learning algorithms and performance achieved is compared with the literature. The dataset is stained for nuclei (DAPI) and a marker for cancer epithelial (panEPI) which was used to create the ground truth. Three popu- lar Convolutional Neural Network (CNN) namely U-Net, Residual U-Net and VGG16 were implemented to perform the segmentation task on the tissue mi- croarray dataset. In addition, a transfer learning approach was tested with the VGG16 network that was pre-trained with ImageNet dataset. Further, the performance from the three networks were compared using 3fold cross-validation. The dice accuracies achieved were 83.32% for U-Net, 88.05% for Residual U-Net and 82.73% for VGG16. These findings suggest that segmentation of cancerous tissue regions, using only the nuclear morphol- ogy, is feasible with high accuracy. Computer vision methods better utilizing nuclear morphology captured by the nuclear stain, are promising approaches to digitally augment the conventional IF marker panels, and therefore offer im- proved resolution of the molecular characteristics for research settings.

Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities. / Doctor of Philosophy / Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.

Dynamical properties of non-equilibrium systems, similar to equilibrium ones, have been shown to obey robust time scaling laws which have enriched the concept of physical universality classes. In the first part of this Dissertation, we present the results of our investigations of some of the critical dynamical properties of systems belonging to the Voter or the Directed Percolation (DP) universality class. To be more precise, we focus on the aging properties of two-state and three-state Potts models with absorbing states and we determine temporal scaling of autocorrelation and autoresponse functions. We propose a novel microscopic model which exhibits non-equilibrium critical points belonging to the Voter, DP and Ising Universality classes. We argue that our model has properties similar to the Generalized Voter Model (GVM) in its Langevin description. Finally, we study the time evolution of the width of interfaces separating different absorbing states. The second part of this Dissertation is devoted to the applications of Machine Learning models in physical systems. First, we show that a trained Convolutional Neural Network (CNN) using configurations from the Ising model with conserved magnetization is able to find the location of the critical point. Second, using as our training dataset configurations of Ising models with conserved or non-conserved magnetization obtained in importance sampling Monte Carlo simulations, we investigate the physical properties of configurations generated by the Restricted Boltzmann Machine (RBM) model. The first part of this research was sponsored by the US Army Research Office and was accomplished under Grant Number W911NF-17-1-0156. The second part of this work was supported by the United States National Science Foundation through grant DMR-1606814. / Doctor of Philosophy / Physical systems with equilibrium states contain common properties with which they are categorized in different universality classes. Similar to these equilibrium systems, non-equilibrium systems may obey robust scaling laws and lie in different dynamic universality classes. In the first part of this Dissertation, we investigate the dynamical properties of two important dynamic universality classes, the Directed Percolation universality class and the Generalized Voter universality class. These two universality classes include models with absorbing states. A good example of an absorbing state is found in the contact process for epidemic spreading when all individuals are infected. We also propose a microscopic model with tunable parameters which exhibits phase transitions belonging to the Voter, Directed Percolation and Ising universality classes. To identify these universality classes, we measure specific dynamic and static quantities, such as interface density at different values of the tunable parameters and show that the physical properties of these quantities are identical to what is expected for the different universal classes. The second part of this Dissertation is devoted to the application of Machine Learning models in physical systems. Considering physical system configurations as input dataset for our machine learning pipeline, we extract properties of the input data through our machine learning models. As a supervised learning model, we use a deep neural network model and train it using configurations from the Ising model with conserved dynamics. Finally, we address the question whether generative models in machine learning (models that output objects that are similar to inputs) are able to produce new configurations with properties similar to those obtained from given physical models. To this end we train a well known generative model, the Restricted Boltzmann Machine (RBM), on Ising configurations with either conserved or non-conserved magnetization at different temperatures and study the properties of configurations generated by RBM. The first part of this research was sponsored by the US Army Research Office and was accomplished under Grant Number W911NF-17-1-0156. The second part of this work was supported by the United States National Science Foundation through grant DMR-1606814.

Yes / Aggressive posts containing symbolic and offensive images, inappropriate gestures along with provocative textual comments are growing exponentially in social media with the availability of inexpensive data services. These posts have numerous negative impacts on the reader and need an immediate technical solution to filter out aggressive comments. This paper presents a model based on a Convolutional Neural Network (CNN) and Binary Particle Swarm Optimization (BPSO) to classify the social media posts containing images with associated textual comments into non-aggressive, medium-aggressive and high-aggressive classes. A dataset containing symbolic images and the corresponding textual comments was created to validate the proposed model. The framework employs a pre-trained VGG-16 to extract the image features and a three-layered CNN to extract the textual features in parallel. The hybrid feature set obtained by concatenating the image and the text features were optimized using the BPSO algorithm to extract the more relevant features. The proposed model with optimized features and Random Forest classifier achieves a weighted F1-Score of 0.74, an improvement of around 3% over unoptimized features.

Companies in the transporting sector are undergoing an important transformation of electrifyingtheir fleets to meet the industry’s climate targets. To meet customer’s requests, keep its marketposition, and to contribute to a sustainable transporting industry, Scania needs to be in frontof the evolution. One aspect of this is to attract customers by providing accurate information anddetecting customer’s opportunities for electrification. Understanding the natural behavior of weatherparameters and their impact on energy consumption is crucial for providing accurate simulations ofhow daily operations would appear with an electric truck. The aim of this thesis is to map weatherparameters impact on energy consumption and to get an understanding of the correlations betweenenergy consumption and dynamic weather data. ML and deep learning models have undergone training using historical data from operations per-formed by Scania’s Battery Electric Vehicles(BEV). These models have been assessed against eachother to ensure that they are robust and accurate. Utilizing the trained models ability to providereliable consumption predictions based on weather, we can extract information and patterns aboutconsumption derived from customised weather parameters. The results show several interesting correlations and can quantify the impact of weather parametersunder certain conditions. Temperature is a significant factor that has a negative correlation withenergy consumption while other factors like precipitation and humidity prove less clear results. Byinteracting parameters with each other, some new results were found. For instance, the effect ofhumidity is clarified under certain temperatures. Wind speed also turns out to be an importantfactor with a positive correlation to energy consumption.