The use of machine learning (ML) in high energy physics has exploded in the past decade. While it has provide impressive improvements across a broad range of use cases, it has typically been limited to uses with data already collected by experiments. I will discuss the challenges involved with the use of ML on FPGAs in trigger and data acquisition systems in general as well as specific...
Machine learning and physics have long been deeply intertwined, and there have been eras when their relationship came to the forefront. Even in today’s revolutionary AI development, physics has played a significant role—for example, in diffusion models. From a physics standpoint as well, an integrative perspective across various specialized domains is provided by innovative new mathematical...
