Production-Ready AI for Physics: Algorithms in Operation

Artificial intelligence is revolutionizing how we approach physics research. From experimental high-energy physics to astronomy, AI research in physics has ballooned over the last decade. However, use of AI algorithms in routine experiements or operations is still picking up momentum. Aside from the algorithm development, they depend on computational infrastructure, scalable systems, and practical implementations that bridge the gap between theoretical potential and real-world application.

This issue brings together work that showcases AI algorithms deployed in production in the context of physics experiments, potentially being used routinely. It features studies demonstrating use cases that go beyond algorithm development and into production deployment. Lessons learned from taking a prototype into production are encouraged: successes that facilitated adoption, unexpected challenges encountered, design decisions that proved critical. By collecting production-level experiences, this issue aims to bridge the gap between R&D and operational practice, benefiting the broader physics community.


This focus issue welcomes contributions in the following areas:

• Work in experimental physics, computational physics, or data analysis that demonstrate AI techniques for a production use-case.

• More than R&D, the issue welcomes work that had to address practical challenges in taking an algorithm to routine use. This includes, but is not limited to the topics below:

• Address computational intractability: where AI technique are used to bypass or ease a computational bottleneck

• Model efficiency: Achieving comparable scientific results with smaller, more efficient models that are suited for production

• Scalable deployment strategies: Computational infrastructure and putting research prototypes to production systems