Seminars for students in Computational Science (CS)

Please feel free to suggest topics for the seminars.  

Seminars for all the master students in Computational Science (CS). 

Previous

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.

Sakarias Frette: The standard model is the most accurate theory to date, with incredible precision measurements done at multiple detectors. It has however some shortcommings, not being able to explain phenomena such as the hierarchy problem, gravity, dark matter, etc.. Additional theories have been put forward to try to cover these issues, but for now, it has yielded no luck.

The strategy until recently has been to take such a model, and do a targeted search, resulting in large exclusion plots and no new physics. This is effective and fast for a single model, but very biased, and takes a lot of time if you want to try on 100 og 1000 models.

My thesis will instead try to apply a semi unsupervised technique to separate out anomalous data such that we can reduce the uninteresting SM background and focus on possible new physics that might be hidden in the data. The data analysis model used is an autoencoder.

Welcome to a new Computational Science seminar. This coming Friday, we have the pleasure of having Lars Nordbryhn, who has, since 2018, been the Norwegian focal point for IBM's Quantum Computing program. Attached there is a poster to promote the event. Feel free to share it with whomever you feel would be interested.

 

Since the format for the talk might be slightly different than usual, there is no specific title/abstract this time.

 

Nowadays, since almost anyone can post on social media, a strict distinction between source and consumer is no longer evident. As a consequence we are exposed to an exponential growing flood of misleading information produced by an uncontrollable crowd of often clueless creators. Although it is well understood that the spread of misinformation leads to fatal consequences, it seems impossible to manually sort dangerous content from the sheer volume of data published on a daily basis.

Natural language processing is widely used to automatically classify suspicious content. Here, the strategy is to create manually labeled training sets and train classifiers to detect the content of interest.

 

This week we have the pleasure of having Helga Bodahl Holmestad, Sigurd Holmsen and Øystein Høistad from SINTEF.

Helga is a senior researcher at SINTEF (with a PhD in particle physics) while Sigurd and Øystein are working on their master of science thesis with supervisors from SINTEF, using machine learning and AI.

They will talk about research and job possibilities at SINTEF (in particular summer jobs with deadline coming up soon for summer 2023, of interest to many of you) as well as topics for Master of Science projects and other job possibilities. 

Helga and Øystein will focus on machine learning applied to real life projects (in particular in connection with civil engineering and large construction plans) while Sigurd will present a project on machine learning applied to learning physical laws.


 

Physicists, computer scientists and mathematicians are increasingly joining the life sciences using their tools to help figuring out how life works.

Brain science is particularly attractive since we have a fairly good understanding of the principles for how individual nerve cells work and how they can be modelled. Now the challenge is to leverage this knowledge to help us understand how networks of thousands, millions and eventually billions of such nerve cells make us perceive, think and feel. In the seminar I will introduce the challenges, and in particular talk about how hundreds of European scientists in the EU Human Brain Project work together to address this formidable and exciting challenge which some call the "holy grail" of neuroscience.

How did two physicists end up working at Norway’s largest bank? Milan completed his PhD in experimental physics at the University of Western Australia in 2007, while Vilde did her master degree in computational physics at the University of Oslo in 2018.

This Wednesdays Karl Henrik will talk about the programming language Julia and exciting things which can be done with this programming language.

Karl Henrik is working on Machine learning applied to quantum mechanical problems for his MSc thesis. 

This week we have the pleasure of having Anne Marthine Rustad and Signe Riemer-Sørensen from SINTEF and the Mathematics and Cybernetics department.

SINTEF is a very important employer in our field of study and we look forward to hear about exciting research and job possibilities. Earlier this semester we forwarded several summer job options from SINTEF. 

Philip Sørli Niane is master of Science student in CS:physics, 2nd year. Pizza thereafter and plenty of time for discussions. This is also a topic of interest for potential master of science thesis projects. 

In the natural sciences we apply the scientific method in order to uncover the basic rules and principles that govern nature: we make observations, formulate hypotheses, test these hypotheses with experiments and develop theories. Rinse and repeat. But what if you are unable to conduct experiments? Or if the underlying rules suddenly change? These are the difficulties an economists are faced with. In my talk I hope to shed some light on general economic thought and how one goes about modelling “the economic machine” that emerges through human interaction. If the time allow, I will also provide an example of economic cost/benefit analysis done by Menon Economics for the Norwegian Coastal Administration.

Pizza after the seminar. 

Industrial research and development are to a great extent the art of extending existing tools and methods with new techniques and data with the objective to optimise and improve operations. Expert Analytics is a Oslo based consultancy working in the fields of data and computational sciences, and in this presentation we will briefly tell you who we are and walk you through a few examples of what we have done for some of our clients. We will also present an internal product development project, where we make sense out of sensors, and where we're planning to open for summer internships next year for help with data exploration.

Stian may also say something about possible master of science thesis projects. He is PhD student.