Fase curricular

Even though spectral rendering is fairly easy to implement, it is still not widely used. Performance issues are only partly a reason. In fact, due to cache coherency and with cheap component-wise illumination calculations, the drop in performance is not significant. Rather, we felt that it is the lack of spectral materials and the difficulty of reusing existing RGB setups and textures within a spectral setup that have posed an obstacle to users.

To close this crucial gap in the design pipeline, we have devised a spectral palette tool that allows the user to create a set of lights and reflectances such that different parts of a rendering can be enhanced, or be made to disappear, in real time and interactively. We formulated the design process as a least squares problem, yielding global minimization of the criteria. The design scheme and optimization is novel in both graphics as well as in colour science. The resulting set of spectra and colours have utility in the visualization of volume data, but their usefulness is not restricted to this arena or to surface graphics. In fact, with the liberty to inject actual physical spectra for any of the components, one may design appropriate lights to attain specific perceived colours when viewing real physical subjects.

Interactive parameter space partitioning

Computational power today enables researchers to build and study algorithmic models that may involve large numbers of variables and complex relations among them. The gist of Chapter 1 was that in order to draw any practically relevant conclusions from a simulation, it remains crucial to ensure a close correspondence between formal model and the real-world system under scrutiny.

A possible step to achieve this correspondence is to match model output with measured field data. However, in early modelling stages such data may not be available and a law- driven approach has to be chosen. Beyond that, even after fitting given observations there may still be free model parameters that can be controlled to adjust the behaviour of the computer simulation. This can happen, if the expressive power of the model exceeds the number of available measurements, or if the measurements are so noisy that several different model instances are equally acceptable. To formally address this case, it is possible to introduce additional regularizing criteria that a solution has to fulfill. This was, for instance, done in the context of our spectral palette design in Equation 5.13 on page 82 by minimizing the curvature of the generated distribution functions. Instead of using numerical criteria, the user could be given a method to interactively tune free parameters of the model to favour more plausible solutions that match prior experience. In such a case, a domain expert could be involved to interactively tune parameters of the model or to prescribe ranges that favour solutions that match prior experience, theoretical insight, or intuition.

Towards that goal, we recognize that the optimization of parameters for some notion of performance is distinct from the objective to discover regions in parameter space that exhibit qualitatively different system behaviour, such as fluid vs. gaseous state, or formation

of various movement patterns in a swarm simulation. Optimization is one focus of statistical methods in experimental design and has great potential for integration with visual tools, as for instance demonstrated recently by Torsney-Weir et al. [TWSM+11].

The focus of this chapter is on the latter aspect of qualitative discovery. This can support the understanding of the studied system, strengthen confidence in the suitability of the modelling mechanisms and, thus, become a substantial aid in the research process.

In the context of modelling this is a novel viewpoint, since typical approaches calibrate one best version of the model and then study how it behaves. To put regional parameter space exploration into practice, a number of challenges have to be overcome. To identify and address those, we (a) performed a field analysis of three application domains and derived a list of requirements in Section 1.2, (b) present paraglide, a system that addresses these requirements with a set of interaction and visualization techniques novel for this kind of application area, (c) conducted a longitudinal field evaluation ofparaglide showing practical benefits. In summary,paraglidesets out to make the following contributions to computational modelling:

• Parameter region construction is promoted as a separate user interaction step dur- ing experimental design. This allows to address different efficiency issues of multi- dimensional sampling.

• A common step in explorative hypothesis formation is the construction of additional dependent feature variables and goal functions. Paraglide facilitates this with inter- preter based back-ends. Also, this seamlessly integrates model code from sources such as MATLAB, R, or Python.

• Qualitatively distinct solutions are identified and the parameter space of the model is partitioned into the corresponding regions. This allows to visually derive global state- ments about the sensitivity of the model to parameter changes, which traditionally is studied locally.

6.1

Background

Research that is related to visual analysis of multi-dimensional data has been discussed in Section 2.4 and specifically Section 2.4.4. Hence, the following review can focus specifically

on user interfaces that adress some aspects of interactive parameter adjustment of compu- tational models. Methods that are more specific to the design of particular components of paraglidewill be discussed in the respective sections of Section 6.2.