Outputs (203)

Gauged 2-form symmetries in 6D SCFTs coupled to gravity (2021)
Journal Article
Braun, A. P., Larfors, M., & Oehlmann, P.-K. (2021). Gauged 2-form symmetries in 6D SCFTs coupled to gravity. Journal of High Energy Physics, 2021(12), Article 132. https://doi.org/10.1007/jhep12%282021%29132

We study six dimensional supergravity theories with superconformal sectors (SCFTs). Instances of such theories can be engineered using type IIB strings, or more generally F-Theory, which translates field theoretic constraints to geometry. Specificall... Read More about Gauged 2-form symmetries in 6D SCFTs coupled to gravity.

Thermal convection with generalized friction (2021)
Journal Article
Straughan, B. (2022). Thermal convection with generalized friction. Annali dell'Universita di Ferrara, 68(1), 63-68. https://doi.org/10.1007/s11565-021-00382-6

A model for thermal convection with generalized friction is investigated. It is shown that the linear instability threshold is the same as the global stability one. In addition, decay of the energy in the L2 norm is shown for the perturbation velocit... Read More about Thermal convection with generalized friction.

Cosmological Scattering Equations (2021)
Journal Article
Gomez, H., Lipinski Jusinskas, R., & Lipstein, A. (2021). Cosmological Scattering Equations. Physical Review Letters, 127(25), Article 251604. https://doi.org/10.1103/physrevlett.127.251604

We propose a worldsheet formula for tree-level correlation functions describing a scalar field with arbitrary mass and quartic self-interaction in de Sitter space, which is a simple model for inflationary cosmology. The correlation functions are loca... Read More about Cosmological Scattering Equations.

Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers (2021)
Book Chapter
Aslett, L. J. M. (2022). Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers. In L. J. Aslett, F. P. Coolen, & J. De Bock (Eds.), Uncertainty in Engineering (15-35). Springer. https://doi.org/10.1007/978-3-030-83640-5_2

Models which are constructed to represent the uncertainty arising in engineered systems can often be quite complex to ensure they provide a reasonably faithful reflection of the real-world system. As a result, even computation of simple expectations,... Read More about Sampling from Complex Probability Distributions: A Monte Carlo Primer for Engineers.

Global Coronal Equilibria with Solar Wind Outflow (2021)
Journal Article
Rice, O. E., & Yeates, A. R. (2021). Global Coronal Equilibria with Solar Wind Outflow. Astrophysical Journal, 923(1), Article 57. https://doi.org/10.3847/1538-4357/ac2c71

Given a known radial magnetic field distribution on the Sun’s photospheric surface, there exist wellestablished methods for computing a potential magnetic field in the corona above. Such potential fields are routinely used as input to solar wind mode... Read More about Global Coronal Equilibria with Solar Wind Outflow.

From dS to AdS and back (2021)
Journal Article
Sleight, C., & Taronna, M. (2021). From dS to AdS and back. Journal of High Energy Physics, 2021(12), Article 74. https://doi.org/10.1007/jhep12%282021%29074

We describe in more detail the general relation uncovered in our previous work between boundary correlators in de Sitter (dS) and in Euclidean anti-de Sitter (EAdS) space, at any order in perturbation theory. Assuming the Bunch-Davies vacuum at early... Read More about From dS to AdS and back.

Introduction to Bayesian Statistical Inference (2021)
Book Chapter
Karagiannis, G. (2022). Introduction to Bayesian Statistical Inference. In L. Aslett, F. Coolen, & J. De Bock (Eds.), Uncertainty in Engineering: Introduction to Methods and Applications (1-13). (1). Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5_1

We present basic concepts of Bayesian statistical inference. We briefly introduce the Bayesian paradigm. We present the conjugate priors; a computational convenient way to quantify prior information for tractable Bayesian statistical analysis. We pre... Read More about Introduction to Bayesian Statistical Inference.

Uncertainty in Engineering - Introduction to Methods and Applications (2021)
Book
Aslett, L., Coolen, F., & De Bock, J. (Eds.). (2022). Uncertainty in Engineering - Introduction to Methods and Applications. Springer Verlag. https://doi.org/10.1007/978-3-030-83640-5

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability th... Read More about Uncertainty in Engineering - Introduction to Methods and Applications.

Counterfactual explanation of machine learning survival models (2021)
Journal Article
Kovalev, M., Utkin, L., Coolen, F., & Konstantinov, A. (2022). Counterfactual explanation of machine learning survival models. Informatica: An International Journal, 32(4), 817-847. https://doi.org/10.15388/21-infor468

A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine le... Read More about Counterfactual explanation of machine learning survival models.

Nonperturbative effects in the Standard Model with gauged 1-form symmetry (2021)
Journal Article
Anber, M. M., & Poppitz, E. (2021). Nonperturbative effects in the Standard Model with gauged 1-form symmetry. Journal of High Energy Physics, 2021(12), Article 55. https://doi.org/10.1007/jhep12%282021%29055

We study the Standard Model with gauged Z(1)N=2,3,6 subgroups of its Z(1)6 1-form global symmetry, making the gauge group SU(3)×SU(2)×U(1)ZN. We show that, on a finite T3, there are self-dual instantons of fractional topological charge. They mediate... Read More about Nonperturbative effects in the Standard Model with gauged 1-form symmetry.