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About RAISE

Research in Artificial Intelligence for Science and Engineering (RAISE) Intiative

Historically, the importance of computing in accelerating discoveries in natural sciences has been noted. Nobel physics laureate Paul Dirac stated in 1929 that “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.” This statement remains to be true today after almost 100 years. For example, in quantum physics, it is known that the Schrödinger's equation provides precise descriptions of quantum systems, but solving such equations is only possible for very small systems due to its exponential complexity. In fluid mechanics, the Navier-Stokes equations describe spatiotemporal dynamics of fluid flows, but solving these equations of practically useful sizes is highly demanding. Recent research has demonstrated that AI can significantly accelerate the solving of these equations, resulting in major advancements in science and engineering.

The primary aim of this RAISE Initiative is to take a first step in building a coordination network within the Texas A&M community at the intersection among (1) foundational AI research, (2) AI for science, and (3) AI for engineering. We hope our efforts will result in foundational advances in AI, a deeper understanding of science, enhanced design of engineering systems, better educational experiences for Aggies, and ultimately ensure that AI benefits all of humanity.

I invite you to join us on this exciting journey and Gig 'em!

Shuiwang Ji
 

Director, The RAISE Initiative
Professor, Computer Science & Engineering
Texas A&M University

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Artificial intelligence (AI) has experienced remarkable progress over the past decade. Recently, AI has started to advance science by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales. Such improved understanding enables us to design and build engineering systems that operate more effectively, efficiently, and safely. From our viewpoint and that of many others, AI paves the way for a new paradigm in making scientific discoveries and building engineering systems.

 

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