Harnessing Machine Learning for Ultrafast Science in Facility Settings: Opportunities and Challenges

Abstract

Ultrafast science is rapidly evolving from a collection of specialized laser experiments into a facility-scale, data-intensive research endeavour. High-repetition-rate femtosecond and attosecond sources, synchronized multidimensional diagnostics, and increasingly nonlinear laser-matter interactions now generate heterogeneous data streams whose volume, speed, and complexity often exceed the capacity of conventional manual workflows. This chapter examines how machine learning can become an enabling layer for next-generation ultrafast facilities, with emphasis on the Extreme Light Infrastructure (ELI) ecosystem. We discuss the scientific drivers for artificial-intelligence-assisted experimentation, propose a facility-scale digital architecture spanning acquisition, ingestion, storage, analytics, machine learning, adaptive feedback, and user interfaces, and review representative examples from ELI ALPS, ELI Beamlines, and ELI-NP. The central thesis is that machine learning should not be treated merely as an offline data-analysis tool; rather, when constrained by physical insight and embedded in robust data infrastructure, it can support real-time diagnostics, surrogate modeling, anomaly detection, experimental optimization, and progressively autonomous discovery workflows.