Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

The full optimization of the design and operation of instruments whose functioning relies on
the interaction of radiation with matter is a super-human task, due to the large dimensionality …

Generating high-quality laser-plasma accelerated electron beams requires carefully
balancing a plethora of physical effects and is therefore challenging—both conceptually and …

Laser wakefield accelerators promise to revolutionize many areas of accelerator science.
However, one of the greatest challenges to their widespread adoption is the difficulty in …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

High-fidelity physics simulations are powerful tools in the design and optimization of
charged particle accelerators. However, the computational burden of these simulations often …

In the field of beam physics, two frontier topics have taken center stage due to their potential
to enable new approaches to discovery in a wide swath of science. These areas are …

Numerical optimization algorithms are already established tools to increase and stabilize the
performance of particle accelerators. These algorithms have many advantages, are …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

In recent work, it has been shown that reinforcement learning (RL) is capable of solving a
variety of problems at sometimes super-human performance levels. But despite continued …