2023

Helga Margrete Bodahl Holmestad from the SINTEF Digital’s Department of Mathematics and Cybernetics. 

Helga, together with Eirik Høyehem (Helga's summer student) and Mari Lindlan (a former CS student working in the optimization group) will share with us:

Mikkel Jensen: 

Various theoretical models and experimental results propose different governing mechanisms for friction at the nanoscale. We consider a graphene sheet modified with Kirigami-inspired cuts and under the influence of strain. Prior research has demonstrated that this system exhibits out-of-plane buckling, which may cause a decrease in contact area when sliding on a substrate.

According to asperity theory, such a decrease in contact area is expected to reduce friction. However, to the best of our knowledge, no previous studies have investigated the frictional behavior of a nanoscale Kirigami graphene sheet subjected to strain.

William Hirst: 

This thesis explores a diverse array of Machine Learning (ML) models as they search for chargino-neutralino pair production in three-lepton final states with missing transverse momentum. The study is based on a data set of sqrt(s) = 13 TeV proton-proton collisions recorded with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139 fb−1. The ML models applied in the study were three variants of Deep Neural Networks (DNN), and Boosted Decision Trees (BDT). The DNN variants included an ordinary dense Neural Network (NN), Parameterized Neural Network (PNN) and ensemble models utilizing pattern-specific pathways created by competing neurons. In the latter variant I included a novel layer introduced in this thesis, the Stochastic-Channel-Out (SCO).

Daniel Johan Aarstein: 

Within fluid mechanics, most interesting phenomena occur on the boundary between fluids of different densities, i.e. water+air, water+oil. Adding the constraints that the two fluids are insoluble, in addition to having the system take place in a pipe, we might experience what is known as a "slug".

Experimental and numerical study of slug behavior is a field within itself, this thesis aims to be a proof-of-concept that a novel, non-intrusive Deep Learning model can be used for real-time analysis. The model itself utilizes a Convolutional Neural Network in order to classify, and predict properties for a given slug in a pipe, based solely on acoustic emission from said pipe.

Current findings indicate that the classification on unseen data has an accuracy of ~93 %. The regression for velocity and length is, however, less precise with R2 scores of ~0.5 and ~0, respectively.

João Inácio: 

The field of low-dimensionality magnetism has developed into an active area of solid-state physics, attracting both theoretical and experimental researchers. Due to the vast array of theoretical tools, there is a large effort to develop a full theoretical understanding of one-dimensional (1D) systems. A large part of this interest is due to nonequilibrium dynamics, where steady-state transport is a generic example.

Many physically relevant 1D models are Bethe-ansatz integrable, such as the spin-1/2 XXZ-model and the Fermi-Hubbard model, but computing transport coefficients still poses a great challenge. Moreover, little is currently known about transport properties of non-integrable models, such as the spin-S XXZ-model or ladder spin systems.