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Breadcrumb

  1. Research
  2. Data Science & Computing
  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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From subsurface transport to ice-sheet modeling, computational mathematicians at Pacific Northwest National Laboratory (PNNL) develop and enable innovative modeling and simulation methodologies that improve the understanding of complex systems.

As leaders in mathematics research, mathematicians at PNNL develop novel computational modeling and data-analysis tools used 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 the fundamental understanding of physical, chemical, and biological principles using computational modeling, experimentation, and data evaluation.

Our work applies fundamental mathematical principles to problems in chemistry, cybersecurity, high-performance computing, and more. We also specialize in applying machine learning and statistics methods to accelerate scientific computations for complex systems, including multiscale, multiphysics, and machine learning algorithm development and implementation. 

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

We design, implement, and validate software, tools, and statistical package products that allow deployable solutions within any research capability. From modeling uncertainty in the power grid and guiding the design of energy systems with simulations—all the way to multiscale modeling for bioremediation to operations research and industrial engineering—our applied mathematics and statistics fuel fundamental understanding of scientific phenomena. PNNL consistently delivers high-quality methods and applications to address critical scientific challenges in national security and energy resilience.

Recent News

AUGUST 8, 2023
Staff Accomplishment

Świrydowicz Receives Best Paper Award

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APRIL 19, 2023
Staff Accomplishment

Karniadakis Named One of the Best Mathematics Scientists

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Photo of George Karniadakis
APRIL 27, 2022
Staff Accomplishment

Kutz Selected as Society for Industrial and Applied Mathematics Fellow

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Research Highlights

APRIL 3, 2024
Article

Got Omics Data? We Have an App for That

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Data scientists developed an analysis application for researchers.
NOVEMBER 7, 2023
Article

The Quest to Identify Genetically Engineered Organisms

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Artistic imagining of engineered DNA
MARCH 2, 2023
Article

Tackling Hard Governing Equations with Machine Learning

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Related Divisions

Advanced Computing, Mathematics, and Data
AI and Data Analytics Division

Related Links

What are Advanced Computing Testbeds?Applied StatisticsComputational EngineeringDiscrete MathematicsOperations Research and Industrial EngineeringUncertainty Quantification and Multifidelity ModelingPhILMsSEA-CROGS

Contacts

Panos Stinis
Mathematician
Panagiotis.stinis@pnnl.gov
206-528-3495
Erin Barker
Senior Research Scientist
erin.barker@pnnl.gov

Lab-Level Communications Priority Topics

Computing

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