Query-driven visualization of time-varying adaptive mesh refinement data

The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduces a new method that directly addresses the dynamic spatial and temporal properties of AMR grids that challenge many existing visualization techniques. Further, we present the first implementation of query-driven visualization on the GPU that uses a GPU-based indexing structure to both answer queries and efficiently utilize GPU memory. We apply our method to two different science domains to demonstrate its broad applicability.     

Detection of environmental changes from network structure

We studied the networks of the temperature record in the atmosphere. They are made by the strength of the synchronization, including the delay between temperatures at locations on Earth. We consider these locations as nodes, and we consider a pair of locations as a link if the synchronization between them is stronger than a threshold. The network is scale-free, which is thought to contribute to the stability of the climate. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Leveraging online communities in fostering adaptive schools

There has long been a call for schools to prepare students for the twenty-first century where skills and dispositions differ significantly from much of what has historically characterized formal education. The knowledge based economy calls for policy and pedagogical efforts that would transform schools. Schools are to foster communities of learners. This paper suggests that para-communities may be points of leverage in the fostering of adaptive schools. A critical analysis is done on the differences between para-communities (such as online communities) and schools; and an argument is made that they each serve differing goals and should be left distinct because they achieve different societal and economic demands.     

Optimization strategy in the development process with application to cast components

At the development of commercial vehicles, it is necessary to develop cost-optimized components with respect to their entire lifetime. This requires a product development process that utilizes the possibility to simulate the product performance using modeling of different degrees of sophistication at any predefined level-of-detail (LOD). For example, the mechanical properties of a cast component depend strongly on the casting process, the simulation of which requires complex modeling at a high LOD. In this article, we propose an optimization strategy that is based on the LOD concept to develop cost-effective components. A sizing optimization of a cast model component is carried out with respect to both the mechanical and thermal response to exemplify the process. In particular, a (novel) global measure of the temperature in energy norm is introduced as an indicator of the “production fitness” to ensure high quality of the material properties. This measure is the thermal counterpart of the well-accepted mechanical strain energy.     

Time dependent processing in a parallel pipeline architecture

Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed-memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.     

Compositional pattern producing networks: A novel abstraction of development

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.

Using difference intervals for time-varying isosurface visualization

We present a novel approach to out-of-core time-varying isosurface visualization. We attempt to interactively visualize time-varying datasets which are too large to fit into main memory using a technique which is dramatically different from existing algorithms. Inspired by video encoding techniques, we examine the data differences between time steps to extract isosurface information. We exploit span space extraction techniques to retrieve operations necessary to update isosurface geometry from neighboring time steps. Because only the changes between time steps need to be retrieved from disk, I/O bandwidth requirements are minimized. We apply temporal compression to further reduce disk access and employ a point-based previewing technique that is refined in idle interaction cycles. Our experiments on computational simulation data indicate that this method is an extremely viable solution to large time-varying isosurface visualization. Our work advances the state-of-the-art by enabling all isosurfaces to be represented by a compact set of operations.     

Self-improving process control for molecular beam epitaxy

This paper addresses manufacturing research involving advances in material process control. The research objective has been to develop intelligent, self-directed and self-improving control systems which enablein situ (real-time) control path generation based on both product (material behaviour) and processing (control agent) feedback. A product-process control philosophy which emphasises product quality is described together with a generic architecture for representing product and process knowledge.Specific details are presented involving the development and application of a self-directed and self-improving material processing system for molecular beam epitaxy of gallium arsenide wafers. Special emphasis is given to the development of a neural model for self-improving control as well as future research directions.