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Computational Mathematics

As a leader in mathematics research, PNNL mathematicians develop novel data-analysis methods and tools to extract hidden features, anomalies, and signatures that support discovery and optimize data-gathering approaches through sampling and experimental design. PNNL's applied mathematics and statistics complement research conducted throughout the laboratory, fueling fundamental understanding of physical, chemical, and biological principles using computational modeling, experimentation, and data evaluation.


Applied Statistics

Highly skilled statisticians, operations researchers, and data scientists focus on developing statistical and mathematical models to provide solutions and to quantify and control uncertainty in support of national security decision makers and enhance scientific discovery.

Computational Engineering

Computational Engineering aims to solve complex engineering and design problems via modeling and simulation. PNNL applies this capability in areas as far-reaching as industrial manufacturing and carbon capture technologies.

Discrete Mathematics

Discrete Mathematics affords models and tools for analyzing real-world phenomena that change abruptly and exist clearly in one state or another.

Operations Research and Industrial Engineering

Operations Research and Industrial Engineering research provides critical operational modeling and risk analysis in a diverse set of areas that impact national security.

Uncertainty Quantification and Multifidelity Modeling

PNNL enables probabilistic and mathematical models to represent and explore stochastic processes and phenomena, especially when lab experiments are too costly, difficult, hazardous, or time-consuming. Computer models can estimate the performance of real-world phenomenon, while model input parameters can be simulated from probability distributions to measure sensitivities and uncertainties of results.

Computational Mathematics and Statistics


Seminar Series