404 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Interfaces and Quantum Algebras, I: Stable Envelopes
The stable envelopes of Okounkov et al. realize some representations of
quantum algebras associated to quivers, using geometry. We relate these
geometric considerations to quantum field theory. The main ingredients are the
supersymmetric interfaces in gauge theories with four supercharges, relation of
supersymmetric vacua to generalized cohomology theories, and Berry connections.
We mainly consider softly broken compactified three dimensional theories. The companion papers will discuss applications of this
construction to symplectic duality, Bethe/gauge correspondence, generalizations
to higher dimensional theories, and other topics.Comment: 152 pages; v2: references added, various explanations improve
Symmetric integrators with improved uniform error bounds and long-time conservations for the nonlinear Klein-Gordon equation in the nonrelativistic limit regime
In this paper, we are concerned with symmetric integrators for the nonlinear
relativistic Klein--Gordon (NRKG) equation with a dimensionless parameter
, which is inversely proportional to the speed of light.
The highly oscillatory property in time of this model corresponds to the
parameter and the equation has strong nonlinearity when \eps is
small. There two aspects bring significantly numerical burdens in designing
numerical methods. We propose and analyze a novel class of symmetric
integrators which is based on some formulation approaches to the problem,
Fourier pseudo-spectral method and exponential integrators. Two practical
integrators up to order four are constructed by using the proposed symmetric
property and stiff order conditions of implicit exponential integrators. The
convergence of the obtained integrators is rigorously studied, and it is shown
that the accuracy in time is improved to be \mathcal{O}(\varepsilon^{3}
\hh^2) and \mathcal{O}(\varepsilon^{4} \hh^4) for the time stepsize \hh.
The near energy conservation over long times is established for the multi-stage
integrators by using modulated Fourier expansions. These theoretical results
are achievable even if large stepsizes are utilized in the schemes. Numerical
results on a NRKG equation show that the proposed integrators have improved
uniform error bounds, excellent long time energy conservation and competitive
efficiency
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
Surrogate models are necessary to optimize meaningful quantities in physical
dynamics as their recursive numerical resolutions are often prohibitively
expensive. It is mainly the case for fluid dynamics and the resolution of
Navier-Stokes equations. However, despite the fast-growing field of data-driven
models for physical systems, reference datasets representing real-world
phenomena are lacking. In this work, we develop AirfRANS, a dataset for
studying the two-dimensional incompressible steady-state Reynolds-Averaged
Navier-Stokes equations over airfoils at a subsonic regime and for different
angles of attacks. We also introduce metrics on the stress forces at the
surface of geometries and visualization of boundary layers to assess the
capabilities of models to accurately predict the meaningful information of the
problem. Finally, we propose deep learning baselines on four machine learning
tasks to study AirfRANS under different constraints for generalization
considerations: big and scarce data regime, Reynolds number, and angle of
attack extrapolation
A conservative exponential integrators method for fractional conservative differential equations
The paper constructs a conservative Fourier pseudo-spectral scheme for some conservative fractional partial differential equations. The scheme is obtained by using the exponential time difference averaged vector field method to approximate the time direction and applying the Fourier pseudo-spectral method to discretize the fractional Laplacian operator so that the FFT technique can be used to reduce the computational complexity in long-time simulations. In addition, the developed scheme can be applied to solve fractional Hamiltonian differential equations because the scheme constructed is built upon the general Hamiltonian form of the equations. The conservation and accuracy of the scheme are demonstrated by solving the fractional Schrödinger equation
Applications of Molecular Dynamics simulations for biomolecular systems and improvements to density-based clustering in the analysis
Molecular Dynamics simulations provide a powerful tool to study biomolecular systems with atomistic detail. The key to better understand the function and behaviour of these molecules can often be found in their structural variability. Simulations can help to expose this information that is otherwise experimentally hard or impossible to attain. This work covers two application examples for which a sampling and a characterisation of the conformational ensemble could reveal the structural basis to answer a topical research question. For the fungal toxin phalloidin—a small bicyclic peptide—observed product ratios in different cyclisation reactions could be rationalised by assessing the conformational pre-organisation of precursor fragments. For the C-type lectin receptor langerin, conformational changes induced by different side-chain protonations could deliver an explanation
of the pH-dependency in the protein’s calcium-binding. The investigations were accompanied by the continued development of a density-based clustering protocol into a respective software package, which is generally well applicable for the use case of extracting conformational states from Molecular Dynamics data
High-order renormalization of scalar quantum fields
Thema dieser Dissertation ist die Renormierung von perturbativer skalarer Quantenfeldtheorie bei groĂźer Schleifenzahl. Der Hauptteil der Arbeit ist dem Einfluss von Renormierungsbedingungen auf renormierte Greenfunktionen gewidmet.
Zunächst studieren wir Dyson-Schwinger-Gleichungen und die Renormierungsgruppe, inklusive der Gegenterme in dimensionaler Regularisierung. Anhand zahlreicher Beispiele illustrieren wir die verschiedenen Größen.
Alsdann diskutieren wir, welche Freiheitsgrade ein Renormierungsschema hat und wie diese mit den Gegentermen und den renormierten Greenfunktionen zusammenhängen. Für ungekoppelte Dyson-Schwinger-Gleichungen stellen wir fest, dass alle Renormierungsschemata bis auf eine Verschiebung des Renormierungspunktes äquivalent sind. Die Verschiebung zwischen kinematischer Renormierung und Minimaler Subtraktion ist eine Funktion der Kopplung und des Regularisierungsparameters. Wir leiten eine neuartige Formel für den Fall einer linearen Dyson-Schwinger Gleichung vom Propagatortyp her, um die Verschiebung direkt aus der Mellintransformation des Integrationskerns zu berechnen. Schließlich berechnen wir obige Verschiebung störungstheoretisch für drei beispielhafte nichtlineare Dyson-Schwinger-Gleichungen und untersuchen das asymptotische Verhalten der Reihenkoeffizienten.
Ein zweites Thema der vorliegenden Arbeit sind Diffeomorphismen der Feldvariable in einer Quantenfeldtheorie. Wir präsentieren eine Störungstheorie des Diffeomorphismusfeldes im Impulsraum und verifizieren, dass der Diffeomorphismus keinen Einfluss auf messbare Größen hat. Weiterhin untersuchen wir die Divergenzen des Diffeomorphismusfeldes und stellen fest, dass die Divergenzen Wardidentitäten erfüllen, die die Abwesenheit dieser Terme von der S-Matrix ausdrücken. Trotz der Wardidentitäten bleiben unendlich viele Divergenzen unbestimmt.
Den Abschluss bildet ein Kommentar ĂĽber die numerische Quadratur von Periodenintegralen.This thesis concerns the renormalization of perturbative quantum field theory. More precisely, we examine scalar quantum fields at high loop order. The bulk of the thesis is devoted to the influence of renormalization conditions on the renormalized Green functions. Firstly, we perform a detailed review of Dyson-Schwinger equations and the renormalization group, including the counterterms in dimensional regularization. Using numerous examples, we illustrate how the various quantities are computable in a concrete case and which relations they satisfy.
Secondly, we discuss which degrees of freedom are present in a renormalization scheme, and how they are related to counterterms and renormalized Green functions. We establish that, in the case of an un-coupled Dyson-Schwinger equation, all renormalization schemes are equivalent up to a shift in the renormalization point. The shift between kinematic renormalization and Minimal Subtraction is a function of the coupling and the regularization parameter. We derive a novel formula for the case of a linear propagator-type Dyson-Schwinger equation to compute the shift directly from the Mellin transform of the kernel. Thirdly, we compute the shift perturbatively for three examples of non-linear Dyson-Schwinger equations and examine the asymptotic growth of series coefficients.
A second, smaller topic of the present thesis are diffeomorphisms of the field variable in a quantum field theory. We present the perturbation theory of the diffeomorphism field in momentum space and find that the diffeomorphism has no influence on measurable quantities. Moreover, we study the divergences in the diffeomorphism field and establish that they satisfy Ward identities, which ensure their absence from the S-matrix. Nevertheless, the Ward identities leave infinitely many divergences unspecified and the diffeomorphism theory is perturbatively unrenormalizable.
Finally, we remark on a third topic, the numerical quadrature of Feynman periods
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of
discoveries in natural sciences. Today, AI has started to advance natural
sciences by improving, accelerating, and enabling our understanding of natural
phenomena at a wide range of spatial and temporal scales, giving rise to a new
area of research known as AI for science (AI4Science). Being an emerging
research paradigm, AI4Science is unique in that it is an enormous and highly
interdisciplinary area. Thus, a unified and technical treatment of this field
is needed yet challenging. This work aims to provide a technically thorough
account of a subarea of AI4Science; namely, AI for quantum, atomistic, and
continuum systems. These areas aim at understanding the physical world from the
subatomic (wavefunctions and electron density), atomic (molecules, proteins,
materials, and interactions), to macro (fluids, climate, and subsurface) scales
and form an important subarea of AI4Science. A unique advantage of focusing on
these areas is that they largely share a common set of challenges, thereby
allowing a unified and foundational treatment. A key common challenge is how to
capture physics first principles, especially symmetries, in natural systems by
deep learning methods. We provide an in-depth yet intuitive account of
techniques to achieve equivariance to symmetry transformations. We also discuss
other common technical challenges, including explainability,
out-of-distribution generalization, knowledge transfer with foundation and
large language models, and uncertainty quantification. To facilitate learning
and education, we provide categorized lists of resources that we found to be
useful. We strive to be thorough and unified and hope this initial effort may
trigger more community interests and efforts to further advance AI4Science
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