NVIDIA launched this week a technology center in the U.K. designed to support groundbreaking research in AI and data science — and foster engagement across the country’s higher education and research community.
Hartree Centre, the University of Edinburgh’s EPCC and the University of Reading are the first to join the NVIDIA AI Technology Center, which provides a collaborative community for world-class talent driving AI adoption and excellence across the U.K.
Center members receive access to NVIDIA expertise and resources to support their projects. The first NVAITC opened in Singapore in 2015 to support collaborative projects throughout the Asia Pacific region.
Initial research projects at the U.K. center will focus on digital twinning of physical objects, simulations for gas turbine modeling, and machine learning-enhanced climate simulations.
Hartree Centre: Creating Virtual Twins of Physical Objects
Hartree Centre applies AI and accelerated computing technologies to industry challenges in businesses of all sizes, across various industry sectors – from healthcare, life sciences and chemistry to engineering and manufacturing.
The Hartree Centre’s collaboration with NVIDIA will focus on digital twinning — the detailed virtual representation of physical assets. This work will enable U.K. industry to develop new products and optimize their processes, boosting productivity and reducing time to market.
“Becoming part of the NVIDIA AI Technology Center will help us to work with our industry and academic networks on more projects supporting the co-creation and adoption of AI solutions,” said Alison Kennedy, director of the Science and Technology Facilities Council at Hartree Centre. “With our internationally recognized expertise in data analytics, leading high performance computing platforms, and focus on innovation and industry impact, we’re ideally situated to help businesses realize benefits in productivity from novel technologies like AI.”
EPCC: Simulating Mechanics, Dynamics of Gas Turbines
EPCC, the HPC center based at the University of Edinburgh, will explore applying AI and trans-precision calculations to the modeling and simulation of complex multi-scale structures.
The project’s goal is to accelerate large-scale mechanical, structural and fluid dynamic simulations that can be applied to gas turbine modeling. Building on recent work by EPCC, the researchers will use a machine learning and trans-precision computing approach to improve the convergence and performance of iterative solvers for linear systems of equations.
“We want to be able to leverage the rapid advances of large-scale machine learning to help traditional supercomputing applications,” said Mark Parsons, director of EPCC and associate dean of e-research. “One area where this has been shown to be very promising is in the preconditioning of iterative solvers, which is where we will start targeting our efforts and exploit the capabilities of GPUs.”
University of Reading: Managing Flood of Data from Weather Simulations
As a member of the NVIDIA technology center, University of Reading will investigate machine learning methods to enhance simulation workflows in weather and climate. Its first project applies deep learning techniques in data-intensive simulations to identify important data structures for subsequent storage.
Such techniques will be necessary to avoid the data deluge arising from better, more complex models. The approach taken in this project will minimize the volume of data that needs to be stored and processed — saving time, energy and hardware costs.
“AI methods are on the brink of revolutionizing computational science in many respects,” said Julian Kunkel, lecturer in the Department of Computer Science at the university. “A distinctive benefit for data-intensive science is that AI will empower scientists to analyze data generated by large-scale simulations effectively reducing the time for scientific breakthroughs. The NVAITC is a crystallization point for industry and academic collaboration. It will accelerate and increase the impact of our research.”