About
The Machine Learning and the Physical Sciences workshop aims to provide an
informal, inclusive and leading-edge venue for research and discussions at the interface of
machine learning (ML) and the physical sciences. This interface spans (1) applications of ML
in physical sciences (
Recent years have seen a tremendous increase in cases where ML models are used for scientific processing and discovery, and similarly, instances where tools and insights from the physical sciences are brought to the study of ML models. The harmonious co-development of the two fields is not a surprise: ML methods have had great success in learning complex representations of data that enable novel modeling and data processing approaches in many scientific disciplines. Indeed, in some sense, ML and physics are concerned with a shared goal of characterizing the true probability distributions of nature. As ML and physical science research becomes more intertwined, questions naturally arise around what scientific understanding is when science is performed with the assistance of complex and highly parameterized models.
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.
A part of the workshop program will be dedicated to a focus area discussing a concrete open question of interest to the community: the role of inductive bias and interpretability in PS with ML, and the role of physically-informed inductive biases in ML models. This critical and ongoing conversation because it gets to the heart of what it means to do science in the era of deep learning. The program will also feature a moderated panel discussion on "Funding and Institutional Support for Machine Learning and Physical Sciences Research" -- a topic of urgent current interest to researchers as the nascent field is rallying for institutional support across university departments, national labs, government-funded AI institutes, and industry. The goal of the panel will be to elucidate for researchers across many career stages the various support mechanisms available for this intersectional field and future prospects.
NeurIPS 2023
The Machine Learning and the Physical
Sciences 2023 workshop will be held on December 15, 2023 at the New Orleans Convention
Center
in New Orleans, USA as a part of the 37th annual conference on
Neural Information Processing Systems (NeurIPS). The workshop is planned to take
place in a hybrid format inclusive of virtual participation.