CosmoVis: An Interactive Visual Analysis Tool for Exploring Hydrodynamic Cosmological Simulations.

We introduce CosmoVis, an open source web-based visualization tool for the interactive analysis of massive hydrodynamic cosmological simulation data. CosmoVis was designed in close collaboration with astrophysicists to enable researchers and citizen scientists to share and explore these datasets, and to use them to investigate a range of scientific questions. CosmoVis visualizes many key gas, dark matter, and stellar attributes extracted from the source simulations, which typically consist of complex data structures multiple terabytes in size, often requiring extensive data wrangling. CosmoVis introduces a range of features to facilitate real-time analysis of these simulations, including the use of "virtual skewers," simulated analogues of absorption line spectroscopy that act as spectral probes piercing the volume of gaseous cosmic medium. We explain how such synthetic spectra can be used to gain insight into the source datasets and to make functional comparisons with observational data. Furthermore, we identify the main analysis tasks that CosmoVis enables and present implementation details of the software interface and the client-server architecture. We conclude by providing details of three contemporary scientific use cases that were conducted by domain experts using the software and by documenting expert feedback from astrophysicists at different career levels.

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks.

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies-called polyphorms-that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

Robust and practical measurement of volume transport parameters in solid photo-polymer materials for 3D printing.

Volumetric light transport is a pervasive physical phenomenon, and therefore its accurate simulation is important for a broad array of disciplines. While suitable mathematical models for computing the transport are now available, obtaining the necessary material parameters needed to drive such simulations is a challenging task: direct measurements of these parameters from material samples are seldom possible. Building on the inverse scattering paradigm, we present a novel measurement approach which indirectly infers the transport parameters from extrinsic observations of multiple-scattered radiance. The novelty of the proposed approach lies in replacing structured illumination with a structured reflector bonded to the sample, and a robust fitting procedure that largely compensates for potential systematic errors in the calibration of the setup. We show the feasibility of our approach by validating simulations of complex 3D compositions of the measured materials against physical prints, using photo-polymer resins. As presented in this paper, our technique yields colorspace data suitable for accurate appearance reproduction in the area of 3D printing. Beyond that, and without fundamental changes to the basic measurement methodology, it could equally well be used to obtain spectral measurements that are useful for other application areas.

Polyphorm: Structural Analysis of Cosmological Datasets via Interactive Physarum Polycephalum Visualization.

This paper introduces Polyphorm, an interactive visualization and model fitting tool that provides a novel approach for investigating cosmological datasets. Through a fast computational simulation method inspired by the behavior of Physarum polycephalum, an unicellular slime mold organism that efficiently forages for nutrients, astrophysicists are able to extrapolate from sparse datasets, such as galaxy maps archived in the Sloan Digital Sky Survey, and then use these extrapolations to inform analyses of a wide range of other data, such as spectroscopic observations captured by the Hubble Space Telescope. Researchers can interactively update the simulation by adjusting model parameters, and then investigate the resulting visual output to form hypotheses about the data. We describe details of Polyphorm's simulation model and its interaction and visualization modalities, and we evaluate Polyphorm through three scientific use cases that demonstrate the effectiveness of our approach.

Real-time screen-space scattering in homogeneous environments.

The proposed screen-space algorithm approximates light scattering in homogeneous participating environments, such as water. Instead of simulating full global illumination, this method models scattering by a physically based point spread function. A discrete hierarchical convolution in a texture MIP map makes the algorithm efficient, and a custom anisotropic incremental filter prevents illumination leaking.