Discovery in design: Designing mirrors for high-power lasers using machine learning

Machine Learning



A plasma photonic structure just a few tens of microns wide evolves over a few picoseconds. Image by University of Strathclyde.

A plasma mirror that can withstand the intensity of powerful lasers is designed through a new machine learning framework.

Physics and computer science researchers at the University of Strathclyde have combined their knowledge of lasers and artificial intelligence to create a technology that could significantly reduce the time it takes to design advanced optical components for lasers. This could pave the way for new discoveries in science.

High-power lasers can be used to develop tools for medicine, manufacturing, and nuclear fusion. However, these have become larger and more expensive due to the size of the optical components, and currently it is necessary to keep the intensity of the laser beam low enough not to damage the optical components. As the peak power of lasers increases, the diameter of mirrors and other optical components must increase from about 1 meter to more than 10 meters. These weigh several tons, making them difficult and expensive to manufacture.

Process acceleration

Researchers have been exploring alternative uses for plasma, the ionized gas that makes up more than 99.9% of the visible universe. Plasma is resistant to damage. This has the potential to reduce mirror size down to millimeters, but the challenge is to design plasma structures that reflect light efficiently and reliably. The researchers accelerated the design process by combining machine learning algorithms and computer models.

The study was published in Nature Communications Physics.

Slav Ivanov, from the School of Computer and Information Sciences at the University of Strathclyde, lead author of the study, said:

Traditional design approaches develop many prototypes that are tested in each cycle to ultimately achieve the goal. This typically involves many iterations, and a complete design process can include hundreds of thousands to millions of iterations. Using machine learning, the number of iterations required to find the optimal design can be reduced to just a few dozen.

Professor Dino Jarosinski from the Department of Physics at the University of Strathclyde, a partner in the research, said: “This research could also be a driving force for discovery. Mirrors can compress pulses by specifying a specific purpose, limited only by our imagination. This was completely unexpected. By investigating why the pulse is compressed, we found that it is due to the time boundary: the plasma layer deforms in a bellows-like manner, adding new frequencies to the reflected pulse and causing it to become compressed.

“This has far-reaching implications. Designs can be tailored to suit our purposes, and new mechanisms may be discovered.”



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