Program:

The school will start on Sunday January 17th with an afternoon lecture, and continue until Friday January 22nd after lunch. The school is adapted to fit well with the train schedule from both Bergen and Oslo. The handout booklet is available here.

The schedule consists of a morning lecture each day, a long mid-day break suitable for social activities and skiing, and two afternoon lectures. In the evening, all participants dine together. There is also a poster session scheduled for Wednesday afternoon.

The topic abstracts are described further down on this page.

Sunday:

16:30 – 17:00: Coffee
17:00 – 17:15: André R. Brodtkorb: Welcome & Introduction
17:15 – 18:45: Introduction to visualization (Helwig Hauser) (Slides on webpage of Tamara Munzner, Visualization Analysis & Design)
18:45 – 19:15: Scientific Visualization Panel
(Helwig Hauser, Andrea Brambilla slides, Ingrid Hotz slides, Gordon Kindlmann slides, Hans-Jörg Schulz slides)
19:30: Dinner

Monday:

09:00 – 10:30: Gordon Kindlmann: Algebraic Visualization
10:30 – 15:00: Break
15:00 – 16:30: Hans-Jörg Schulz: Information Visualization (ppt, pdf, password is foo!bar where you replace foo with geilo and bar with the year)
16:30 – 17:00: Coffee
17:00 – 18:30: Andrea Brambilla: Paraview tutorial 1
19:30: Dinner

Tuesday:

09:00 – 10:30: Ingrid Hotz:Topology-based visualization 1
10:30 – 15:00: Break
15:00 – 16:30: Hans-Jörg Schulz: Interaction (large ppt, pdf, password is foo!bar where you replace foo with geilo and bar with the year)
16:30 – 17:00: Coffee
17:00 – 18:30: Andrea Brambilla: Paraview tutorial 2
19:30: Dinner

Wednesday:

09:00 – 10:30: Torsten Möller: Visualization tools for decision making 1
10:30 – 15:00: Break
15:00 – 16:30: Ingrid Hotz: Topology-based visualization 2
16:30 – 17:00: Coffee
17:00 – 18:30: Gordon Kindlmann: Diderot
18:30 – 19:15: Poster session
19:30: Dinner

Thursday:

09:00 – 10:30: Hans-Jörg Schulz: Visual analytics (large ppt, pdf, password is foo!bar where you replace foo with geilo and bar with the year)
10:30 – 15:00: Break
15:00 – 16:30: Torsten Möller: Visualization tools for decision making 2
16:30 – 17:00: Coffee
17:00 – 18:30: Andrea Brambilla: Paraview tutorial 3
19:30: Dinner

Friday:

09:00 – 10:30: Ingrid Hotz: Tensor visualization
10:30 – 11:00: Break (Remember check out by 11:00)
11:15 – 12:45: Gordon Kindlmann: Space-scale particles
13:00 – 14:30: Lunch
15:19 Train to Oslo
15:41 Train to Bergen

Software and material

Diderot:
Install Diderot for the wednesday lecture by Gordon Kindlmann. Follow instructions here. (Please note that this is aimed at OSX / Linux users)

Paraview tutorial:
Download sample data (36 MB)
Install Paraview 4.4 from http://www.paraview.org/download/

The topics of the winter school are found below. Please note that the absracts are subject to change, and will be updated regularly. 

Interactive tutorial on Paraview

Lecturer: Andrea Brambilla

Abstract: This will be an interactive tutorial, with exercises, for using ParaView for scientific visualization. ParaView is an open-source software that supports analysis and visualization tasks over large 2D and 3D data sets. By exploiting the features of the Visualization Toolkit (VTK) library, ParaView is able to handle a moltitude of data formats, provides extensive data processing capabilities, and implements a large number of rendering modalities. The tutorial will start by introducing the basic architecture of ParaView and the fundamental concepts required for working with this software. Participants will learn to set up a visualization pipeline and to tune the available rendering techniques. Selected data representations and data processing filters will be presented. The Python scripting capabilities of ParaView will also be discussed, and participants will have the chance to solve several hands-on excercises. The tutorial is mainly targeting beginners, and no previous experience with the application is required. A basic knowledge of Python can be helpful for solving the most advanced excercises.

Visualization in the Computational Sciences

 Lecturer: Gordon Kindlmann

Tentative Abstract: This session will show how a range of techniques in different areas of visualization can be understood and evaluated with some common underlying ideas. We will establish a functional vocabulary for talking about what does and doesn't work in a visualization, and because we believe the value of a visualization lies in its practical application, we propose general principles for good visualization design.

We will further show visualization of symmetric second-order tensors in two and three dimensions using superquadric glyphs, gracefully and unambiguously depicting any combination of positive and negative eigenvalues. We will cover example uses including stress tensors from mechanics, geometry tensors and Hessians from image analysis, and rate-of-deformation tensors in computational fluid dynamics.

We will also introduce and demonstrate Diderot: a Domain-Specific Language for Portable Parallel Scientific Visualization and Image Analysis. The basic idea is to start with Taylor's theorem and Newton's method of root-finding, and to use that to show why we shade isosurfaces with the gradient of the function; how Levoy's 2D transfer function is derived from Newton's method; how generalizations of the same approach are used to visualize extremal features including ridges/valleys and maxima/minima; and how extremal features of scalar attributes of vector and tensor fields are used to show their structure.

Finally, we will present Scale-Space Particles, and why scale-space matters for visualizing and extracting features. We will start by reviewing some of the related knowledge from computer vision and statistics, and continue by showing how it can be used for biomedical applications, such as extracting structure from chest CT scans. This will be based on the Teem software, which is released under an open source lisence.

Relevant Links

• An Algebraic Process for Visualization Design 
• Superquadric Glyphs for Symmetric Second-Order Tensors
• Diderot: a Domain-Specific Language for Portable Parallel Scientific Visualization and Image Analysis
• Sampling and Visualizing Creases with Scale-Space Particles

Feature based methods in scientific visualization

Lecturer: Ingrid Hotz

Abstract: With the increasing complexity of data, efficient methods for filtering and abstraction of the data become more and more important. Feature based methods, which try to extract interesting structures or patterns form the data, are one way to approach this challenge. In this lecture we will discuss different concepts of feature extraction in scientific applications. Thereby we will focus on methods motivated by examples from fluid mechanics and mechanical engineering. In detail we will cover the following topics:

Topological data analysis: The use of topological methods for visualization has lead to many very powerful techniques for feature extraction. In this lecture I will give an introduction to basic topological methods for scalar and vector fields. We will discus the use of these methods for some examples in the field of fluid mechanics.

Tensor field visualization for mechanical engineering: In comparison to vector field visualization the state of the art in tensor field visualization is much less advanced. The notion of features is less clear and thus data filtering relies more on exploration. In this part of the lecture we will start with an introduction to tensor fields and basic tensor visualization methods. Then we will discuss one specific application to highlight related challenges.

Visual Analytics and Interaction

Lecturer: Hans-Jörg Schulz

Human-Computer Interaction: As visual representation realizes the communication of data and computational results from the computer to the user, so does interaction realize the communication of the users' intentions and tasks to the computer. Only in concert, visual representation and interaction form the bidirectional communication between computer and user that is called visualization. While the visual communication channel from the computer to the user has a high throughput, the channels facilitated by interaction (mouse clicks, key presses, touch gestures) are rather limited. This imbalance is the origin of many of the scientific questions in interaction research for visualizations. This lecture will provide an overview over interaction and its research challenges based on the model of communication. For those challenges most relevant to visualization, it will then discuss exemplary solution approaches and illustrate them with software demos and videos.

Visual Analytics: Visual Analytics is the science of analytical reasoning supported by interactive visual interfaces. To this end, it realizes a tight integration of algorithmic computation and interactive visual exploration. This lets the computer and the human user each do what they can do best, thereby achieving faster and deeper insight into data than any discipline could by itself. Well, at least in theory... Achieving such a tight integration with a fluid back and forth between computer and user poses a tremendous computational, visual, and ergonomical challenge in practice. After covering the fundamentals of Visual Analytics, this lecture will look specifically at these challenges,
present current State-of-the-Art approaches to address them, and exemplify them by showcasing Visual Analytics systems.

Information Visualization
Information visualization is often defined as visualization of data without a given spatial reference, as opposed to scientific visualization which depicts data that has such a spatial reference. Yet this distinction becomes hard to uphold as nowadays already 60%-80% of all data is geospatially referenced – including document collections and network data, whose depiction is usually considered to be part of Information Visualization. Reason enough to have a look into this field of visualization to better understand the particularities of depicting data classes, such as text, hierarchies and networks, and categorical data, as well as data with additional properties, such as multi-scale, multi-level, or multi-dimensional data. This lecture will give a short introduction into the field of information visualization, before presenting visualization solutions for the key data classes and data properties that are most often encountered in the wild.

Visual Tools for Decision Making

Lecturer: Torsten Möller

Abstract: This session will highlight the power of visual supported analysis of computational simulations. I will first give a detailed example from our own work -- a tool for fisheries managers (Vismon). Based on an analysis of numerous other tools we have created as well as a review of 21 different application scenarios, I will make the case that visual support greatly facilitates the understanding of these complex systems leading to faster workflows and a better decision process. Some of the challenges to be tackled include the communication of uncertainty as well as the consideration of multiple objectives.

Visualization of magnetically induced current densities in molecules, Heike Fliegl, Centre for Theoretical and Computational Chemistry (CTCC), University of Oslo.

Applied Topology Optimization for Large Scale Problems, Said Zeidan, Department of Mechanical Engineering, Technical University of Denmark.

Interhemispheric climate variability in a pre-industrial control simulation of CCSM4, Jonathan Rheinlænder, Department of Earth Science, Bjerknes Centre, ice2ice, and Prof. Markus Jochum.

Patient Specific Modeling of Cardiac Mechanics, Henrik Nicolay Finsberg, Simula Research Laboratory.

Visualization methods for fracture flow problems, Piotr Olkiewicz, Polish Geological Institute - NRI.

Kalkulo Expert Slutions (poster 1, poster 2), Yvon Halbwachs, Kalkulo AS.

Scalable Heterogeneous CPU-GPU Computations for Unstructured Tetrahedral Meshes, Johannes Langguth, Simula.

Visualizing and comparing In-Situ, Model and EO data, Aleksander Vines, Nansen Environmental and Remote Sensing Center.

Cloud Lightning project, Anne C. Elster, NTNU.

NTNU HPC-Lab GPU-Based Snow Simulator, Anne C. Elster, Inge Halsaune and Thomas Schmid.

Predictive Statistical Analysis of Cardiac Structural and Functional Remodelling, Kristin Mcleod, Simula research laboratory.