Solving life's great mysteries often requires detective work, using observed outcomes to determine their cause. For instance, nuclear physicists at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility analyze the aftermath of particle interactions to understand the structure of the atomic nucleus.
This type of subatomic sleuthing is known as the inverse problem. It is the opposite of a forward problem, where causes are used to calculate the effects. Inverse problems arise in many descriptions of physical phenomena, and often their solution is limited by the experimental data available.
That's why scientists at Jefferson Lab and DOE's Argonne National Laboratory, as part of the QuantOm Collaboration, have led the development of an artificial intelligence (AI) technique that can reliably solve these types of puzzles on supercomputers at large scales.
"We set out to prove we could use generative AI to better understand the structure of the proton," said Jefferson Lab Data Scientist Daniel Lersch, a lead investigator on the study. "But this framework isn't bound to nuclear physics. Inverse problems can be anything."
The system is called SAGIPS (Scalable Asynchronous Generative Inverse-Problem Solver). It relies on high-performance computing and generative AI models, which can produce new text, images or videos based on data the algorithms are trained on.
SAGIPS was built for QuantOm. Its goal is to better understand fundamental nuclear physics by using advanced computational methods, and the SAGIPS system was recently featured in the journal Machine Learning: Science and Technology.
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