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Computational science

Theorists have always developed mathematical models to attempt to gain insight into how physical systems operate. However, rapid advances in computational capabilities over the last several decades have enabled the creation of high-fidelity simulation software based on those models, which are then exercised as a proxy for learning about the real world. This new approach, which serves as a complement to pure theory and experiment, has come to be known as computational science.

Effective computational science research requires expertise in mathematical modeling, numerical analysis, software engineering, high-performance computing, and statistics, as well as a deep understanding of the technical application area under study. As a result, it is a deeply interdisciplinary endeavor, requiring the combined efforts of computer scientists, mathematicians, statisticians, and application scientists.

At NIST, computational scientists work to predict properties of atomic, chemical, biological, and material systems from first principles, as well as for engineered systems, such as buildings and communication networks. Others use computation to study how fires and their contaminants spread within buildings and at the wildland-urban interface. NIST mathematicians work to develop more efficient and accurate numerical methods that enable higher fidelity simulations, computer scientists develop techniques and tools to map such computations onto modern parallel and distributed computing systems, and to visualize the often complex data that emerges. More mature research efforts can result in the distribution of well-engineered software enabling members of the broader scientific community to perform simulation studies of their own.

Awards

2016 AAAS Fellow - Ron Boisvert

The American Association for the Advancement of Science (AAAS), the world's largest general scientific society, has elected Ron Boisvert of...

News and Updates

Projects and Programs

Temporal Computing

The human brain does some types of information processing, like speech recognition, image recognition, or video processing, much more efficiently than can be

Spintronics for Neuromorphic Computing

One of the most promising new approaches to next generation information processing is spintronics, where information is carried with electronic spin rather than

Visualization

ITL researchers realize that visualization of scientific data can provide an understanding of the phenomenon or data being studied. Visualization can be

Publications

Notes on Interrogating Random Quantum Circuits

Author(s)
Luis Brandao, Rene C. Peralta
Consider a quantum circuit that, when fed a constant input, produces a fixed-length random bit- string in each execution. Executing it many times yields a

Summary: Workshop on Machine Learning for Optical Communication Systems

Author(s)
Joshua A. Gordon, Abdella Battou, Michael P. Majurski, Dan Kilper, Uiara Celine, Massimo Tonatore, Joao Pedro, Jesse Simsarian, Jim Westdorp, Darko Zibar
Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are

Software

Network Modeling Software

This software is a set of NetworkX additions for the creation of graphs to model networks.Graphicality Testing -- This is a set of routines for testing if a