Transport in complex systems : a lattice Boltzmann approach

Celem niniejszej pracy jest zbadanie możliwości efektywnego modelowania procesów transportu w złożonych systemach z zakresu dynamiki płynów za pomocą metody siatkowej Boltzmanna (LBM). Złożoność systemu została potraktowana wieloaspektowo i konkretne układy, które poddano analizie pokrywały szeroki zakres zagadnień fizycznych, m.in. przepływy wielofazowe, hemodynamikę oraz turbulencje. We wszystkich przypadkach szczególna uwaga została zwrócona na aspekty numeryczne — dokładność używanych modeli, jak również szybkość z jaką pozwalają one uzyskać zadowalające rozwiązanie. W ramach pracy rozwinięty został pakiet oprogramowania Sailfish, będący otwarta implementacja metody siatkowej Boltzmanna na procesory kart graficznych (GPU). Po analizie szybkości jego działania, walidacji oraz omówieniu założeń projektowych, pakiet ten został użyty do symulacji trzech typów przepływów. Pierwszym z nich były przepływy typu Brethertona/Taylora w dwu- i trójwymiarowych geometriach, do symulacji których zastosowano model energii swobodnej. Analiza otrzymanych wyników pokazała dobra zgodność z danymi dostępnymi w literaturze, zarówno eksperymentalnymi, jak i otrzymanymi za pomocą innych metod numerycznych. Drugim badanym problemem były przepływy krwi w realistycznych geometriach tętnic dostarczających krew do ludzkiego mózgu. Wyniki symulacji zostały dokładnie porównane z rozwiązaniem otrzymanym metoda objętości skończonych z wykorzystaniem pakietu OpenFOAM, przyspieszonego komercyjna biblioteka pozwalająca na wykonywanie obliczeń na GPU. Otrzymano dobra zgodność między badanymi metodami oraz pokazano, że metoda siatkowa Boltzmanna pozwala na wykonywanie symulacji do ok. 20 razy szybciej. Trzecim przeanalizowanym zagadnieniem były turbulentne przepływy w prostych geometriach. Po zwalidowaniu wszystkich zaimplementowanych modeli relaksacji na przypadku wiru Kidy, zbadano przepływy w pustym kanale oraz w obecności przeszkód. Do symulacji wykorzystano zarówno siatki zapewniające pełną rozdzielczość aż do skal Kolmogorova, jak i siatki o mniejszej rozdzielczości. Również w tym kontekście pokazano dobrą zgodność wyników otrzymanych metodą siatkową Boltzmanna z wynikami innych symulacji oraz badaniami eksperymentalnymi. Pokazano również, że implementacja LBM w pakiecie Sailfish zapewnia większą stabilność obliczeń niż ta opisana w literaturze dla tych samych przepływów i modeli relaksacji

Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades

The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models

Fast micromagnetic simulations on GPU : recent advances made with mumax³

In the last twenty years, numerical modeling has become an indispensable part of magnetism research. It has become a standard tool for both the exploration of new systems and for the interpretation of experimental data. In the last five years, the capabilities of micromagnetic modeling have dramatically increased due to the deployment of graphical processing units (GPU), which have sped up calculations to a factor of 200. This has enabled many studies which were previously unfeasible. In this topical review, we give an overview of this modeling approach and show how it has contributed to the forefront of current magnetism research

DFT-based microscopic magnetic modeling for low-dimensional spin systems

In the vast realm of inorganic materials, the Cu2+-containing cuprates form one of the richest classes. Due to the combined effect of crystal-field, covalency and strong correlations, all undoped cuprates are magnetic insulators with well-localized spins S=1/2, whereas the charge and orbital degrees of freedom are frozen out. The combination of the spin-only nature of their magnetism with the unique structural diversity renders cuprates as excellent model systems. The experimental studies, boosted by the discovery of high-temperature superconductivity in doped La2CuO4, revealed a fascinating variety of magnetic behaviors observed in cuprates. A digest of prominent examples should include the spin-Peierls transition in CuGeO3, the Bose-Einstein condensation of magnons in BaCuSi2O6, and the quantum critical behavior of Li2ZrCuO4. The magnetism of cuprates originates from short-range (typically, well below 1 nm) exchange interactions between pairs of spins Si and Sj, localized on Cu atoms i and j. Especially in low-dimensional compounds, these interactions are strongly anisotropic: even for similar interatomic distances |Rij|, the respective magnetic couplings Jij can vary by several orders of magnitude. On the other hand, there is an empirical evidence for the isotropic nature of this interaction in the spin space: different components of Si are coupled equally strong. Thus, the magnetism of cuprates is mostly described by a Heisenberg model, comprised of Jij(Si*Sj) terms. Although the applicability of this approach to cuprates is settled, the model parameters Jij are specific to a certain material, or more precisely, to a particular arrangement of the constituent atoms, i.e. the crystal structure. Typically, among the infinite number of Jij terms, only several are physically relevant. These leading exchange couplings constitute the (minimal) microscopic magnetic model. Already at the early stages of real material studies, it became gradually evident that the assignment of model parameters is a highly nontrivial task. In general, the problem can be solved experimentally, using elaborate measurements, such as inelastic neutron scattering on large single crystals, yielding the magnetic excitation spectrum. The measured dispersion is fitted using theoretical models, and in this way, the model parameters are refined. Despite excellent accuracy of this method, the measurements require high-quality samples and can be carried out only at special large-scale facilities. Therefore, less demanding (especially, regarding the sample requirements), yet reliable and accurate procedures are desirable. An alternative way to conjecture a magnetic model is the empirical approach, which typically relies on the Goodenough-Kanamori rules. This approach links the magnetic exchange couplings to the relevant structural parameters, such as bond angles. Despite the unbeatable performance of this approach, it is not universally applicable. Moreover, in certain cases the resulting tentative models are erroneous. The recent developments of computational facilities and techniques, especially for strongly correlated systems, turned density-functional theory (DFT) band structure calculations into an appealing alternative, complementary to the experiment. At present, the state-of-the-art computational methods yield accurate numerical estimates for the leading microscopic exchange couplings Jij (error bars typically do not exceed 10-15%). Although this computational approach is often regarded as ab initio, the actual procedure is not parameter-free. Moreover, the numerical results are dependent on the parameterization of the exchange and correlation potential, the type of the double-counting correction, the Hubbard repulsion U etc., thus an accurate choice of these crucial parameters is a prerequisite. In this work, the optimal parameters for cuprates are carefully evaluated based on extensive band structure calculations and subsequent model simulations. Considering the diversity of crystal structures, and consequently, magnetic behaviors, the evaluation of a microscopic model should be carried out in a systematic way. To this end, a multi-step computational approach is developed. The starting point of this procedure is a consideration of the experimental structural data, used as an input for DFT calculations. Next, a minimal DFT-based microscopic magnetic model is evaluated. This part of the study comprises band structure calculations, the analysis of the relevant bands, supercell calculations, and finally, the evaluation of a microscopic magnetic model. The ground state and the magnetic excitation spectrum of the evaluated model are analyzed using various simulation techniques, such as quantum Monte Carlo, exact diagonalization and density-matrix renormalization groups, while the choice of a particular technique is governed by the dimensionality of the model, and the presence or absence of magnetic frustration. To illustrate the performance of the approach and tune the free parameters, the computational scheme is applied to cuprates featuring rather simple, yet diverse magnetic behaviors: spin chains in CuSe2O5, [NO]Cu(NO3)3, and CaCu2(SeO3)2Cl2; quasi-two-dimensional lattices with dimer-like couplings in alpha-Cu2P2O7 and CdCu2(BO3)2, as well as the 3D magnetic model with pronounced 1D correlations in Cu6Si6O18*6H2O. Finally, the approach is applied to spin liquid candidates --- intricate materials featuring kagome-lattice arrangement of the constituent spins. Based on the DFT calculations, microscopic magnetic models are evaluated for herbertsmithite Cu3(Zn0.85Cu0.15)(OH)6Cl2, kapellasite Cu3Zn(OH)6Cl2 and haydeeite Cu3Mg(OH)6Cl2, as well as for volborthite Cu3[V2O7](OH)2*2H2O. The results of the DFT calculations and model simulations are compared to and challenged with the available experimental data. The advantages of the developed approach should be briefly discussed. First, it allows to distinguish between different microscopic models that yield similar macroscopic behavior. One of the most remarkable example is volborthite Cu3[V2O7](OH)2*2H2O, initially described as an anisotropic kagome lattice. The DFT calculations reveal that this compound features strongly coupled frustrated spin chains, thus a completely different type of magnetic frustration is realized. Second, the developed approach is capable of providing accurate estimates for the leading magnetic couplings, and consequently, reliably parameterize the microscopic Hamiltonian. Dioptase Cu6Si6O18*6H2O is an instructive example showing that the microscopic theoretical approach eliminates possible ambiguity and reliably yields the correct parameterization. Third, DFT calculations yield even better accuracy for the ratios of magnetic exchange couplings. This holds also for small interchain or interplane couplings that can be substantially smaller than the leading exchange. Hence, band structure calculations provide a unique possibility to address the interchain or interplane coupling regime, essential for the magnetic ground state, but hardly perceptible in the experiment due to the different energy scales. Finally, an important advantage specific to magnetically frustrated systems should be mentioned. Numerous theoretical and numerical studies evidence that low-dimensionality and frustration effects are typically entwined, and their disentanglement in the experiment is at best challenging. In contrast, the computational procedure allows to distinguish between these two effects, as demonstrated by studying the long-range magnetic ordering transition in quasi-1D spin chain systems. The computational approach presented in the thesis is a powerful tool that can be directly applied to numerous S=1/2 Heisenberg materials. Moreover, with minor modifications, it can be largely extended to other metallates with higher value of spin. Besides the excellent performance of the computational approach, its relevance should be underscored: for all the systems investigated in this work, the DFT-based studies not only reproduced the experimental data, but instead delivered new valuable information on the magnetic properties for each particular compound. Beyond any doubt, further computational studies will yield new surprising results for known as well as for new, yet unexplored compounds. Such "surprising" outcomes can involve the ferromagnetic nature of the couplings that were previously considered antiferromagnetic, unexpected long-range couplings, or the subtle balance of antiferromagnetic and ferromagnetic contributions that "switches off" the respective magnetic exchange. In this way, dozens of potentially interesting systems can acquire quantitative microscopic magnetic models. The results of this work evidence that elaborate experimental methods and the DFT-based modeling are of comparable reliability and complement each other. In this way, the advantageous combination of theory and experiment can largely advance the research in the field of low-dimensional quantum magnetism. For practical applications, the excellent predictive power of the computational approach can largely alleviate designing materials with specific properties.:List of Figures List of Tables List of Abbreviations 1. Introduction 2. Magnetism of cuprates 3. Experimental methods 4. DFT-based microscopic modeling 5. Simulations of a magnetic model 6. Model spin systems: challenging the computational approach 7. Kagome lattice compounds 8. Summary and outlook Appendix Bibliography List of publications Acknowledgment

Approximate inference on graphical models: message-passing, loop-corrected methods and applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure