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Breadcrumb

  1. Research
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  3. Computational Mathematics & Statistics

Computational
Mathematics
& Statistics

Solving problems with
statistics and mathematics
 

Pacific Northwest National Laboratory researchers use nuclear magnetic resonance spectrometers—basically superconducting magnets—to discern between the different chemical structures in fuels. Each chemical interacts with the magnetic field in a different way. Statistician and modeler Alejandro Heredia-Langner links this data to the influence that the chemical structure has on important fuel properties.

Andrea Starr | Pacific Northwest National Laboratory

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How can our nation predict the behavior of complex natural and man-made systems and find critical signatures amid overwhelming data? Pacific Northwest National Laboratory (PNNL) helps decision makers be better informed when making choices in mission-critical domains.

Pacific Northwest National Laboratory (PNNL) has one of the largest and most accomplished teams of statisticians and computational scientists in the U.S. Department of Energy’s national laboratory complex. As leaders in mathematics research, mathematicians at PNNL develop novel computational modeling and data-analysis tools to predict and control complex systems and extract hidden features, anomalies, and signatures that support discovery and optimize data-gathering approaches through sampling and experimental design.

Our applied mathematics and statistics capabilities complement research conducted throughout the laboratory, fueling fundamental understanding of physical, chemical, and biological principles using computational modeling, experimentation, and data evaluation.

Through rigorous analysis, we turn science and programmatic challenges into mathematical problems. An iterative process, our work in signature discovery spans exploratory data analysis, formal experimentation, and multiscale, multiphysics, and machine learning algorithm development and implementation. We build effective and efficient strategies for understanding signatures and correctly identifying when a phenomenon is present. 

PNNL is advancing mathematical theory to build strategically intelligent and resilient computational systems. We develop reasoning within artificial intelligence systems to sense application environments and adapt algorithms to optimally intersect with a dynamic system state.

Under a unifying theme of doing more with less, we design effective resource utilization strategies. PNNL researchers develop solutions for deploying limited resources, implementing effective supply chains, and analyzing concepts of operations to manage risks and achieve operational objectives in the face of uncertainties.

We design, implement, and validate software, tools, and statistical package products that allow deployable solutions within any research capability. From multiscale modeling for bioremediation to operations research and industrial engineering, all the way to climate research and human behavior modeling, our applied mathematics and statistics fuel fundamental understanding of physical, chemical, and biological principles using computational modeling, experimentation, and data evaluation. PNNL consistently delivers high-quality methods and applications to address critical scientific challenges in national security and energy resilience.

Recent News

APRIL 27, 2022
Staff Accomplishment

Kutz Selected as Society for Industrial and Applied Mathematics Fellow

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Headshot of Nathan Kutz
JANUARY 19, 2021
Staff Accomplishment

Machine Learning to Automate Red Teaming for Cyber-Physical Systems

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Red teaming for cyber-physical systems
OCTOBER 26, 2020
Feature

Help Wanted: The Cybersecurity Workforce of the Future Starts with Students Today

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Research Highlights

FEBRUARY 19, 2020
Research Highlight

Creating better models to predict subsurface water flow and transport

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river soil

Related Divisions

Advanced Computing, Mathematics, and Data
Computing and Analytics Division

Related Links

What are Advanced Computing Testbeds?Job OpeningsApplied StatisticsComputational EngineeringDiscrete MathematicsOperations Research and Industrial EngineeringUncertainty Quantification and Multifidelity ModelingPhILMs

Contacts

Panos Stinis
Mathematician
Panagiotis.stinis@pnnl.gov
206-528-3495
Erin Barker, PhD
Group Leader, Applied Statistics & Computational Mathematics
Erin.Barker@pnnl.gov

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