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A

SIMULA FACTS

Figure A.1 Simula’s organisation.

The highest body at Simula Research Laboratory is the board of directors, which

is appointed by the owners of Simula at the General Assembly. Adhering to the pro-

visions of the Companies Act, this board makes strategic decisions and approves

the budget and annual reports. It appoints the managing director, who in turn organ-

ises the company’s activities. The company is divided into three units corresponding

A. Tveito et al. (eds.), Simula Research Laboratory, DOI 10.1007/978-3-642-01156-6,c© Springer-Verlag Berlin Heidelberg 2010

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638 A Simula Facts

to Simula’s three main tasks: Basic Research organises research activities, Research

Education is administratively responsible for the PhD students and postdoctoral fel-

lows, and Research Applications is responsible for promoting the application of the

research results.

Each of these units has a unit director and, with the managing director and the

director of the Administration unit, the directors constitute Simula’s management

group.

Basic Research is divided into three research departments, each with its own de-

partment head. All full-time researchers and project leaders are employed here.

Research Education streamlines the formation of PhD students and postdoctoral

fellows. It consists of the Simula School of Research and Innovation and with Simula

as the majority stakeholder. A PhD student at Simula will be affiliated with SSRI

and will report administratively to this unit. The student’s day-to-day work, scien-

tific research, and supervision, however, take place in the corresponding research

department in Basic Research.

Research Applications consist of the two wholly owned subsidiaries Simula Inno-

vation and Kalkulo. There are two advisory bodies reporting directly to the managing

director: the Scientific Advisory Board, and the Strategic Advisory Group.

Ownership, board and management1

OwnershipSimula Research Laboratory is a limited company jointly owned by the Norwegian

government (80%), Norwegian Computing Center (10%), and SINTEF (10%).

The Board of DirectorsThe board of directors consists of the following members:

• Ingvild Myhre, Chair of the Board

• Anne-Brit Kolstø, University of Oslo

• Gunnar Hartvigsen, University of Tromsø

• Hilde Tonne, Telenor

• Mats Lundqvist, Chalmers School of Entrepreneurship

• Åshild Grønstad Solheim, PhD student, Simula

• Bjørn Fredrik Nielsen, Research Scientist, Simula

Corporate Management• Professor Aslak Tveito, Managing Director of Simula Research Laboratory

• Professor Olav Lysne, Director of Basic Research

1 As of 1 May 2009.

A Simula Facts 639

• Dr. Kristin Vinje, Director of Research Education and the Simula School of Re-

search and Innovation

• Cand. Jur., LLM. Ottar Hovind, Director of Research Applications and Director

of Administration

Management• Dr. Joakim Sundnes, Assistant Director of Basic Research

• Professor Are Magnus Bruaset, Assistant Director of the Simula School of Re-

search and Innovation

• Dr. Åsmund Ødegård, Assistant Director of Administration and IT Manager

• Professor Carsten Griwodz, Head of the Networks and Distributed Systems De-

partment

• Professor Hans Petter Langtangen, Head of the Scientific Computing Depart-

ment, including the Centre of Biomedical Computing

• Dr. Stein Grimstad, Head of the Software Engineering Department

• Dr. Audun Fosselie Hansen, Director of Simula Innovation

• Dr. Christian Tarrou, Director of Kalkulo

B

EXTERNAL CONTRIBUTORS

Bjarne Røsjø

Bjarne Røsjø is a freelance science writer and communica-

tions consultant. A biologist from the University of Oslo,

Røsjø has worked as a journalist, editor, and advisor in the

Norwegian news media and at the Research Council of Nor-

way.

Dana Mackenzie

Dana Mackenzie holds a PhD in Mathematics from Prince-

ton University and works as a freelance mathematics and

science writer. Mackenzie has written several books and arti-

cles in publications such as Science, Smithsonian Magazine,

and New Scientist.


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642 B External Contributors

Christian Hambro

Christian Hambro is an advisor and attorney on matters

of administrative and business law, including environmen-

tal law, tort law, labour law, and international law. Hambro

was director of the Norwegian Research Council from 1994

to 2004.

Anna Godson

Anna Godson holds a degree in Russian and history from

Oxford University. Godson works as a freelance translator

and also proofreads and edits English texts for a number of

clients, including Simula, Telenor, and the Research Council

of Norway.

Sverre Christian Jarild

Sverre Christian Jarild is a freelance photographer with a

background in press photography. Jarild has also worked

with photography in advertising and corporate communica-

tions, as well as documentary projects.

Morten Brakestad

Morten Brakestad is a freelance photographer and holds a

degree in fine art photography from the Glasgow School of

Art. Brakestad has worked as a freelance photographer for

newspapers and public relations agencies.

C

COLOUR FIGURES

Figure C.1 Throughput in presence of link faults, both under a saturated and unsaturated condition

The vertical line marks the transition from guaranteed fault tolerance on the left side to probable

fault tolerance on the right side. (This is a colour version of figure 14.5 on page 148.)


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644 C Colour Figures

Figure C.2 Average packet latency at various generation times for uniform traffic, where various

components of latency are broken down as follows: QL is Queue Latency, NL is Network Latency,

and MTL is Maximum Token Latency. The vertical bars represent the start and the end of the

reconfiguration respectively. (This is a colour version of figure 14.6 on page 152.)

Figure C.3 The transmembrane potential (mV) at four stages. during normal propagation. (This is

a colour version of figure 20.2 on page 270.)

C Colour Figures 645

Figure C.4 Four stages during ischemic propagation. (This is a colour version of figure 20.4 on

page 272.)

Figure C.5 The extracellular potential around the ischemic area during a) the ST segment and b)

the TP segment. The heart boundary is indicated by the solid line. (This is a colour version of figure

20.5 on page 272.)


Figure C.6 A snapshot of the transmembrane potential during the early phase of depolarisation.

Red tissue is in the resting phase while the blue tissue is depolarised. The right figure shows iso-

surfaces of the electrical potential in the torso. (This is a colour version of figure 20.7 on page 273.)

Figure C.7 The location of the true ischemic region and its estimates computed with noise-free and

noisy (synthetic) observation data. Note that the position is recovered rather accurately whereas

the size of the lesion is underestimated, an issue that should be further explored. (This is a colour

version of figure 22.2 on page 299.)


Figure C.8 Results obtained in 3D for a heart in torso model with synthetic observation data. The

figures show the “true” and recovered shifts h in the transmembrane potential. The numbers above

the individual panels quantify the volume perturbations of the heart model used in the inverse

solution procedure. The size of the heart is scaled with respect to the size of the panels. (This is a

colour version of figure 22.3 on page 304.)


Figure C.9 A tetrahedrisation of the human torso generated from MR images. In addition to the

ventricles, our patient-specific models contain lungs. (This is a colour version of figure 22.6 on page

306.)

Figure C.10 The ischemic region computed for patient 013 with our inverse ECG model (22.22)-

(22.23). The result is consistent with the scintigraphic images shown in figure 22.9. More precisely,

the recovered shift h in the transmembrane potential is shown. Blue indicates healthy tissue (h ≈100mV) and red ischemic tissue (h ≈ 50mV). (This is a colour version of figure 22.8 on page 307.)


Figure C.11 A 3D scintigram from patient 013 displaying the uptake of the radioisotope

technetium-99m in the left heart chamber. The upper two traces show the uptake in horizontal

slices during exercise and the next two traces show the uptake during rest. In the lower right panel

the same information is depicted on frontal slices and in the left panel on sagittal slices. By compar-

ing the upper and lower two tracings of the sagittal images, it can be seen that there is less uptake in

the right and lower parts of the exercise recordings than at rest, corresponding to reversible inferior

and apical ischemia. (This is a colour version of figure 22.9 on page 308.)


Figure C.12 Inverse solutions computed with the ECG recorded on patient 013 with different ge-

ometries. The numbers above each panel specify which geometry was used to produce the result

shown. For example, 001 specifies that the geometrical model of patient 001 was employed. More

precisely, the estimated shifts h in the transmembrane potential are shown. Blue indicates healthy

tissue (h ≈ 100mV) and red ischemic tissue (h ≈ 50mV).(This is a colour version of figure 22.10 on

page 309.)

Figure C.13 A vertical section through the Earth exposing the rigid lithosphere and the flowing

asthenosphere. The sketch also illustrates the process of lithospheric stretching, by which the litho-

sphere is thinned to the point of fracturing or breakup, and molten magma arises from the mantle

to form a mid-oceanic ridge. The artwork is courtesy of DK Images [55]. (This is a colour version of

figure 40.1 on page 557.)


Figure C.14 Top: Using the reference solution with five per cent noise added, the green dots indi-

cate the paths of the Landweber iterates in the α1-α2 parameter space for four different starting

values. In all cases the iterates converge towards a minimum. This minimum is located in the neigh-

bourhood of the reference parameters (α1,α2) = (0.8,0.8), which are marked with a red dot.

Bottom: The four parameters have been calibrated for 50 cases in which the reference solution has

been perturbed by 5% random noise. The averages (red dots) approximate the underlying reference

parameters (α1,α2) = (0.6,1.0), (β1,β2) = (1.0,0.6) quite well. The blue dots indicate computed

values for α1 and α2, whereas the green dots refer to values of β1 and β2. The illustrations are

courtesy of Hans-Joachim Schroll. (This is a colour version of figure 40.4 on page 568.)


Figure C.15 Expected values (left) and standard deviation (right) of the volume fraction of sand in a

Dionisos model for a prograding delta. An even mix of sand, shale and silt is injected into the model

at x = 0 km, y = 125 km and builds a delta out onto the continental shelf. In the mean outcome, sand

remains on the contintental shelf and slope, although certain model outcomes allow for significant

sand volumes on the lower slope, as is shown on the right. Together these two pictures capture the

probability distribution of model outcomes. The illustrations are courtesy of Stuart Clark et al. (This

is a colour version of figure 40.7 on page 575.)

Figure C.16 The Petromod model for the Vøring basin consists of 40 layers with 16 different litholo-

gies. The geometry is embedded in a cubic domain covering 96.8 km × 90.8 km × 34.1 km with a

horizontal resolution of 400 meters. The computational problem is defined on a grid with 1.5 million

nodes, leading to 7.3 million unknowns. The Vøring data set is courtesy of StatoilHydro. (This is a

colour version of figure 40.8 on page 578.)


Figure C.17 Top, left to right: The computed distance field φ and the parameter distribution field

P . Bottom, left to right: First and second components of the gradient field ∇φ (∂φ/∂x and ∂φ/∂y).

The fields are associated with a simple parabolic curve and are presented in the context of parallel

deformation. The illustration is courtesy of Øyvind Hjelle. (This is a colour version of figure 40.11

on page 583.)


Figure C.18 When a data set is loaded into memory, only parts of the associated persistent tree

structure D (all nodes, regardless of colour) will be present. This subtree is denoted T (grey and

green nodes), and only the nodes enabled for visualisation (green nodes) will contribute to the

rendering of the scene. Be aware that the grey nodes conceptually will be in memory, even though

all or parts of the data values are not yet loaded. (This is a colour version of figure 40.14 on page

587.)

Figure C.19 Left: This two-layer model consists of topography data with the water-covered areas

cut away combined with a global surface indicating the bottom of the lower crust. Right: This stack

of horizons from the North Sea has only local coverage. The illustrations are courtesy of Trond

Vidar Stensby et al. [45]. (This is a colour version of figure 40.15 on page 588.)


Figure C.20 A high-resolution surface covering a small region in the North Sea has been sewn into

a data set for the global topography. The illustration is courtesy of Trond Vidar Stensby et al. (This

is a colour version of figure 40.16 on page 589.)

Figure C.21 In the 4DLM, grid-based data can be moved with geological time according to an

underlying rotation model. Here, this feature is illustrated by the positions of the tectonic plates

for Africa and South America, including regional data sets for base salt layers outside Angola and

Brazil. The images show the spatial positions today (left) and 114 million years ago (right). The

illustrations are courtesy of Trond Vidar Stensby et al. (This is a colour version of figure 40.17 on

page 590.)


Figure C.22 The 4DLM can rotate both grid-based and vector data, provided that appropriate plate

identifiers have been assigned. These images show the locations of isochrons in the South Atlantic

today (left) and 60 million years ago (right). The illustrations are courtesy of Trond Vidar Stensby

et al. (This is a colour version of figure 40.18 on page 591.)

Figure C.23 The flow line shows the time-dependent track of points that originated from a user-

selected point at the mid-oceanic ridge in the South Atlantic. This illustration is courtesy of Trond

Vidar Stensby et al. (This is a colour version of figure 40.19 on page 591.)

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