Session

Description Motivated by the remarkable success of artificial intelligence (AI) and machine learning (ML) in the fields of computer vision and natural language processing, over the last decade there has been a host of successful applications of AI/ML to a variety of scientific domains. In most cases, the models are trained using the traditional offline (or post hoc) approach, wherein the training data is produced, assembled, and curated separately before training is deployed. While more straightforward, the offline training workflow can impose some important restrictions to the adoption of ML models for scientific applications. To solve these limitations, in situ (or online) ML approaches, wherein ML tasks are performed concurrently to the ongoing simulation, have recently emerged as an attractive new paradigm. In this minisymposium, we explore novel approaches to enable the coupling of state-of-the-art simulation codes with different AI/ML techniques. We discuss the open-source software libraries that are being developed to solve the software engineering challenges of in situ ML workflows, as well as the methodologies adopted to scale on modern HPC systems and their applications to solve complex problems in different computational science domains.