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U.S. DOE Funds Research on AI Models of Physics Simulations

Climate change is a serious problem and extreme climate events are rare and hard to predict. Hence governments and industries need to prepare for the worst-case scenarios. A new collaboration between the University of Chicago and Argonne National Laboratory researchers will apply Artificial Intelligence (AI) to accelerate the scientific simulation of complex physical systems, with the potential to more accurately determine the probability of these extremes.

U.S. Department of Energy (DOE) funded the project with a $3.25 million grant. The project will explore the fundamentals of “surrogate models” — simplified models built using AI that speed up the complex scientific models for climate, energy infrastructure, and other systems.

By allowing researchers to run many more simulations in the same amount of time, these surrogates enable better quantification of the risk of extreme events, the use of computer modelling in rapid decision-making, and other advantages. The project will also draw from other AI applications to find new ways of creating and training surrogate models.

The University of Chicago is committed to working with our national laboratory partners on large-scale problems like climate change, which can only be addressed through these types of scientific collaborations across disciplines and institutions. AI technology can play a key role in addressing challenging issues that have global impact.

– Juan de Pablo, UChicago Vice President for National Laboratories, Science Strategy, Innovation, and Global Initiatives

The most advanced computer models for studying the Earth’s climate, fluid dynamics, or the operation of the national power grid run on Partial Differential Equations (PDEs), mathematical equations used to describe physical laws. But these equations are difficult to solve, and it may require hundreds of hours on the world’s most powerful supercomputers to run a single simulation.

Surrogate models offer an AI-fueled alternative. Instead of running the model, scientists feed the initial conditions and results of past model runs as data into a machine learning model, such as a neural network, which learns to replicate the predictions of the full supercomputer simulation. Once trained, these AI models produce results many times faster than the original, allowing scientists to better characterise the range of possible outcomes through repeated trials.

The UChicago/Argonne team is well suited to shoulder the multidisciplinary breadth of the project, which spans from mathematical foundations to cutting edge data and computer science concepts in artificial intelligence to modelling applications in many different scientific fields.

Collaborations between UChicago and the national labs allow leveraging broad sets of skills across disciplines to address these challenges. The seed funding in the area of AI for Science has been critical in terms of encouraging us to form partnerships and define grand challenges that can only be addressed through large collaborative efforts.

As reported by OpenGov Asia, U.S. DOE also advanced Computational and Data Infrastructures (CDIs) – such as supercomputers, edge systems at experimental facilities, massive data storage, and high-speed networks – are brought to bear to solve the nation’s most pressing scientific problems.

The problems include assisting in astrophysics research, delivering new materials, designing new drugs, creating more efficient engines and turbines, and making more accurate and timely weather forecasts and climate change predictions.

Under a new DOE grant, the project aims to advance the knowledge of how simulation and machine learning (ML) methodologies can be harnessed and amplified to improve the DOE’s computational and data science.

The project will add three important capabilities to current scientific workflow systems — (1) predicting the performance of complex workflows; (2) detecting and classifying infrastructure and workflow anomalies and “explaining” the sources of these anomalies; and (3) suggesting performance optimisations. To accomplish these tasks, the project will explore the use of novel simulation, ML, and hybrid methods to predict, understand, and optimise the behaviour of complex DOE science workflows on DOE CDIs.

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