Authentic Science

• Observations
  • accurately recorded
  • honestly reported
  • assumptions
  • biases
• Conjectures
  • what if?
  • consistent vs. conclusive?
• Collaborations
  • peers
  • mentors
  • experts
• Choosing the right
  • tools
  • techniques
  • technologies
• Facilitate hypothesis building at the right time
• Experiments: Learn-by-doing numerical, visual investigations
• Differences between Observations and Conclusions

Matching Algorithm, Application and Architecture

Modern, scientifically valid models must be built on a firm foundation drawing from computation, theory, and experiment. Confidence in the computational aspects of such models is built from proper matching of the computer algorithm and architecture to the application.

The Progression from Qualitative to Quantitative Models

The learning curve for numerical modeling, as pictured here, may start with a qualitative "hand-waving" model -- for which the visualization is the model-- where simple theory and experimental data give rough insight into behavior. This insight may be refined by formulating the theory as mathematical models and then solving these models with a range of increasingly sophisticated tools, matching the tools to the complexity of the problem. The progression to more quantitative models begins with running other people's models, then modifying these models, and ultimately creating new models, tuning the numerical solutions and visualizations along the way.