# Computational Mathematics

Leveraging mathematical models to quantify and control scientific uncertainty to further scientific discovery. Scientific research and development is a process of gaining fundamental understanding of physical, chemical, and biological principles through computational modeling, experimentation, and data evaluation. As a leader in applied mathematics research, we develop novel data-analysis methods to extract hidden features, anomalies, and signatures from high-dimensional, large-volume, multimedia data in support of discovery and confident decision-making. We develop methods and tools to optimize data-gathering approaches through sampling and experimental design.

Scientific research and development is a process of gaining a fundamental understanding of physical, chemical, and biological principles through computational modeling, experimentation, and data evaluation. The mathematical underpinnings provide a common thread for modeling across disciplinary boundaries and multi-scale systems. Regardless of the application, some level of uncertainty exists and statistical science provides approaches for quantifying and controlling uncertainty while extracting the nuggets that further scientific discovery.

PNNL is a leader in innovative applied statistics and mathematics research. We develop novel data analytic methods to extract hidden features, anomalies, and signatures from high-dimensional, large-volume, multimedia data in support of discovery and confident decision-making. Complex mathematical and stochastic models are developed to represent physical, chemical, biological, and nuclear phenomena. We design experiments and sampling campaigns to increase confident decisions and explicitly manage and quantify uncertainty.

## Related Technologies & Applications

- Uncertainty Quantification
- Multiscale Methods
- Human Behavior Modeling
- Materials Modeling and Design

**POC**: Alex Tartakovsky