Research issues in scientific visualization

As an emerging research discipline, scientific visualization is developing those trappings that demonstrate growth. New algorithms are just beginning to effectively handle the recurring scientific problem of data collected at nonuniform intervals. Volume visualization today is being extended from examining scientific data to reconstructing scattered data and representing geometrical objects without mathematically describing surfaces. Fluid dynamics visualization affects numerous scientific and engineering disciplines. It has taken its place with molecular modeling, imaging remote-sensing data, and medical imaging as a domain-specific visualization research area. Recently, much progress has come from using algorithms with roots in both computer graphics and machine vision. One important research thread has been the topological representation of important features. Volume and hybrid visualization now produce 3D animations of complex flows. However, while impressive 3D visualizations have been generated for scalar parameters associated with fluid dynamics, vector and especially tensor portrayal has proven more difficult. Seminal methods have appeared, but much remains to do. Great strides have also occurred in visualization systems. The area of automated selection of visualizations especially requires more work. Nonetheless, the situation has much improved, with these tools increasingly accessible to scientists and engineers.<<ETX>>