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mathematical sciences, Computational Sciences & Mathematics

PNNL’s Computing Research portfolio—spanning from basic to applied—includes data and computational engineering, high-performance computing, applied mathematics, semantic and human language technologies, machine learning, data and computing architectures, systems integration, and software and application development.
At PNNL, scientists, engineers, programmers, and researchers work together to apply advanced theories, methods, algorithms, models, evaluation tools and testbeds, and computational-based solutions address complex scientific challenges affecting energy, biological sciences, the environment, and national security.

VGC-MM

Taming Big Data Analytics Workloads

To help cope with the unprecedented amount of rapidly changing data that needs to be processed in emerging data analytics applications, Vito Giovanni Castellana and Marco Minutoli, from PNNL’s High Performance Computing group, will showcase their work with the developer framework, “SHAD: the Scalable High-performance Algorithms and Data-structures Library,” at the upcoming IEEE/ACM CCGrid 2018 conference in Washington D.C.



AL

Lumsdaine Honored with 2018 Better Scientific Software Fellowship

Andrew Lumsdaine, Chief Scientist of NIAC and a PNNL Laboratory Fellow, was named to the inaugural class of Better Scientific Software Fellows. Known as BSSw, the Better Scientific Software community is dedicated to advancing computational science and engineering and related technical computing areas. The BSSw Fellowship Program recognizes leaders and advocates of high-quality scientific software.



RNNs

Training Day

Combining deep learning with physical science, scientists from PNNL’s Computational Mathematics and National Security Data Science groups created a framework to solve ordinary differential equations using neural networks. Their model combines machine learning with physics-based models to maximize neural networks’ ability to approximate, or “learn,” unknown functions via training and creates an effective solver implemented in Google’s TensorFlow code.



CE-DFA

Lightweighting with a Heavy Hitter

In a new project under the LightMAT Directed Funding Assistance program, ACMD Division’s Computational Engineering group will work closely with General Motors Co. to develop a predictive performance model of dissimilar metallic spot welds for joining aluminum to steel. This marks the latest collaboration between the automotive leader and PNNL’s computational engineers seeking innovations in automotive materials lightweighting.



SJY

Young Adds New Outlook to the SIAM Committee on Science Policy

Stephen J. Young, a scientist with PNNL’s Discrete Mathematics Team, recently joined the Society for Industrial and Applied Mathematics Committee on Science Policy. The committee promotes applied mathematics research by bringing together diverse appointees from among SIAM’s membership to share their expertise with organizations that coordinate science policy or advocate for science funding. His three-year term began in January 2018.



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