## Machine Learning and the Physical Sciences Workshop

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.


## Topics
In this workshop, we aim to bring together physical scientists and machine learning researchers who work at the intersection of these fields by

- applying machine learning to problems in the physical sciences -- physics, chemistry, astronomy, earth science, biophysics, and related sciences; and
- using physical insights to understand and/or improve machine learning techniques, for instance building hybrid machine learning algorithms that leverage physical models with machine learning blocks to create interpretable and accurate predictive models.

To this end, we encourage external contributions, which will be presented during in-person poster sessions during the workshop. Selected contributions will be offered 15-minute contributed talks. We invite researchers to submit original work in the following areas or areas related to them:

- ML for Physics: Innovative applications of machine learning to the physical sciences; Machine learning model interpretability for obtaining insights into physical systems; Automating/accelerating elements of the scientific process (experimental design, data collection, statistical analysis, etc.).
- Physics in ML: Strategies for incorporating scientific knowledge or methods into machine learning models and algorithms; Applications of physical science methods and processes to understand, model, and improve machine learning models and algorithms.
- Other areas: Any other area related to the subject of the workshop, including but not limited to probabilistic methods that are relevant to physical systems, such as deep generative models, scientific foundation models, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.