Heterogeneous Scientific Data across an Interface Johannes Kehrer, 1 - - PowerPoint PPT Presentation

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Jun 17, 2023 167 likes •374 views

Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface Johannes Kehrer, 1 Philipp Muigg, 2,3 Helmut Doleisch, 2 and Helwig Hauser 1 1 Department of Informatics, University of Bergen, Norway 2 SimVis GmbH, Vienna, Austria

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Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface

Johannes Kehrer,1 Philipp Muigg,2,3 Helmut Doleisch,2 and Helwig Hauser1

1 Department of Informatics, University of Bergen, Norway 2 SimVis GmbH, Vienna, Austria 3 Institute of Computer Graphics & Algorithms,

Vienna University of Technology, Austria

SLIDE 2

Single-part scenarios

 given in a coherent form

(spatio-temporal / multi-variate)

Multi-part scenarios

2 or more related data parts different simulation models different data sources various data grids different dimensionality

(2D/3D data)

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Heterogeneous Scientific Data

time-dependent 3D data coupled climate model

SLIDE 3

Multi-physics Simulations

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cooler aluminum foam heat exchange

Example: fluid-structure interactions (FSIs)

movable or deformable structure  fluid flexible roofs, bridges, blood flow in arteries, etc.

[Bungartz & Schäfer 2006]

SLIDE 4

Sample Analysis of Heat Transfer

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How is the heating (solid) influenced by surrounding vortical flow?

vortex feature

feature transfer

SLIDE 5

Coupled climate models

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Multi-part Scenarios (cont.)

[ Böttinger, ClimaVis08 ]

Land

SLIDE 6

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Multi-part Scenarios (cont.)

3D time-dependent multi-run simulation data data distribution per cell

Data in scientific visualization

data values f(x) (e.g., temperature, pressure values) measured/simulated wrt. a domain x (e.g., 2D/3D space, time, simulation input parameters)

Dimensionality reduction

(e.g., computing statistics wrt. time / spatial axes)

Reintegrate statistics into IVA through attribute derivation

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Visual Analysis across an Interface

Multiple linked views framework [Doleisch et al. ’03] Integrate 2 related data parts Common level of data abstraction

 degree-of-interest attribution (DOIi  [0, 1])

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data part1 data part2

feature transfer

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Relate grid cells across data parts

(no resampling necessary)

DOI transfer Automatic update

f feature spec.

Analysis strategies for 2-part scenarios

Interface

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data part1 data part2

feature transfer

feature refinement data part1 data part2

SLIDE 9

Interface (structural relation)

multi-run data aggregated

relate grid cells weight values for DOI transfer many-to-many relation

ne-to-many

relation

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Degree-of-Interest (DOI) Transfer

weighted sum maximum weighted DOI values maximum DOI value

ther model-based transfer

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Case Study: Multi-run climate Data

size: IQR

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Cooling event (8200 yrs ago)

cean simulation

(2D sections) 10 x 10 = 100 runs time-dependent (250 time steps)

Compute statistics wrt. the multiple runs

median, quartiles, etc. billboard glyphs

[Lie et al. 2009]

year 100

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Case Study: Multi-run climate Data

size: IQR

Johannes Kehrer

Cooling event (8200 yrs ago)

cean simulation

(2D sections) 10 x 10 = 100 runs time-dependent (250 time steps)

Compute statistics wrt. the multiple runs

median, quartiles, etc. billboard glyphs

[Lie et al. 2009]

year 100 q1 q2 q3

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Visual Analysis of Multi-run Data

Starts at aggregated level

(summary statistics) specify features via brushing derive new attributes

Refine features at multi-run level (details)

Investigate further

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multi-run data aggreg. aggregated data multi-run data

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Visual Analysis of Multi-run Data

Starts at aggregated level

(summary statistics) specify features via brushing derive new attributes

Refine features at multi-run level (details)

Investigate further

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multi-run data aggreg. aggregated data multi-run data

SLIDE 15

Outlier Analysis: Aggregated Data

Derive total number of outlier Where are outlier located? (% outlier > 3rd quartile + 1.5 IQR) – (% outlier < 1st quartile – 1.5 IQR)

multi-run data aggregated data aggregated data year 60

SLIDE 16

Feature Refinement: Multi-run Data

Derive measure of outlyingness

[Kehrer et al. 2010]

multi-run data aggr.

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SLIDE 17

Feature Refinement: Multi-run Data

Derive measure of outlyingness

[Kehrer et al. 2010]

multi-run data aggr.

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SLIDE 18

Sensitivity Analysis

multi-run data aggregated data multi-run data

SLIDE 19

Joint visual analysis across two data parts Bidirectional feature transfer via interface Workflow for analyzing 2 data parts simultaneously

multi-run data  aggregated statistics fluid  structure

Aggregated statistics and measures of outlyingness

Conclusions

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Acknowledgements

Thomas Nocke, Michael Flechsig Peter Filzmoser Andreas Lie, Stian Eikeland, Cagatay Turkay, Armin Pobitzer Matthew Parker, Brendan McNulty, David Horn FSI data courtesy of Innovative Computational Engineering GmbH VisGroup Bergen many others