BrightFonts participated in an international symposium on Big Data and Predictive Computational Modeling
High-Dimension: Physical models are characterized by high-dimensional inputs and outputs. Dimensionality reduction is generally a necessary step in enabling the simulation of such systems. Overcoming the curse of dimensionality is a common objective within the Computational Mathematics UQ community and Computational Statistics machine learning community. This is tightly connected with the ability to identify salient input-output features that enable the construction of predictive models even when limited data is available.
Uncertainty: Predictive modeling and uncertainty quantification are more than just another research direction relevant to science and engineering. They constitute a different way of thinking that impacts practically all aspects of scientific and engineering analysis and design. Rather than deriving deterministic answers to complex problems, distributions (error-bars) are obtained that account for our incomplete and often inaccurate information about the problems of interest. An important objective centers around the development of probabilistic frameworks for systems identification, model validation, analysis, design, optimization and control under uncertainty.