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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.


2016 AAAS Fellow - Ron Boisvert

Ron Boisvert is recognized for his distinguished contributions to the fields of mathematical software and computational science, excellence

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

High Performance Computing

Our High Performance Computing (HPC) program enables work on challenging problems that are beyond the capacity and capability of desktop computing resources

News and Updates


Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators

Mathieu Riou, Jacob Torrejon, Flavio Abreu Araujo, Sumito Tsunegi, Guru Khalsa, Damien Querlioz, Paolo Bortolotti, Nathan Leroux, Danijela Markovic, Vincent Cros, K. Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier
Present artificial intelligence algorithms require extensive computations to emulate the behavior of large neural networks, operating current computers near

Optoelectronic Intelligence

Jeff Shainline
To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural


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