About
The Machine Learning and the Physical Sciences workshop aims to provide an informal, inclusive, and leading-edge venue for discussing research and challenges at the intersection of machine learning (ML) and the physical sciences (PS). This includes the applications of ML to problems in the physical sciences (ML for PS) as well as developments in ML motivated by physical insights (PS for ML).
Physical sciences are defined inclusively, including but not limited to physics, astronomy, cosmology, chemistry, biophysics, materials science, and Earth science.
Recent years have highlighted unique opportunities as well as challenges in incorporating ML workflows as part of the scientific process in many physical sciences. For example, fields focused on fundamental physics discovery, such as particle physics and cosmology, often have stringent requirements for exactness, robustness, and latency that go beyond those typically encountered in other scientific domains and industry applications. Data preservation and workflow reproducibility are other central challenges that need to be addressed in the era of large experiments, collaborations, and datasets. In these fields and others, simulations play a central role in connecting theoretical models to observations. The ubiquity and increasing complexity of simulators in PS has spurred methodological advances in ML, e.g. in simulation-based inference and differentiable programming, that are finding applications far beyond PS, showcasing the bidirectional nature of the PS-ML intersection.
The breadth of work at the intersection of ML and physical sciences is answering many important questions for both fields while opening up new ones that can only be addressed by a joint effort of both communities. By bringing together ML researchers and physical scientists who apply and study ML, we expect to strengthen the much needed interdisciplinary dialogue, introduce exciting new open problems to the broader community, and stimulate the production of new approaches to solving challenging open problems in the sciences. Invited talks from leading individuals in both communities will cover the state-of-the-art techniques and set the stage for this workshop, which will also include contributed talks selected from submissions.
The invited talks program will showcase unique features of the physical sciences that highlight current challenges and bidirectional opportunities in ML and PS. This includes the central role of simulators in the scientific process, the need for rigorous uncertainty quantification, and the development of hardware-software co-design solutions for real-time inference.
A part of the workshop program will be dedicated to the focus area discussing the role of data-driven vs inductive bias-driven methods in machine learning and the physical sciences, centering the emerging role of foundation models and their complementarity with approaches leveraging physical inductive biases. This will feature an overview talk, followed by a moderated panel discussion.
NeurIPS 2024
The Machine Learning and the Physical
Sciences 2024 workshop will be held on December 15, 2024 at the Vancouver Convention
Center
in Vancouver, BC, Canada as a part of the 38th annual
conference on
Neural Information Processing Systems (NeurIPS). The workshop is planned to take
place in a hybrid format inclusive of virtual participation.