Machine Learning and the Physical Sciences, NeurIPS 2022

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Machine Learning and the Physical Sciences
Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS)
December 3, 2022
2022 2021 2020 2019 2017
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 (
ML for physics
), (2) developments in ML motivated by physical insights (
physics for ML
), and most recently (3) convergence of ML and physical sciences (
physics with ML
) which inspires questioning what scientific understanding means in the age of complex-AI powered science, and what roles machine and human scientists will play in developing scientific understanding in the future.
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. Taken to the extreme, if an ML model is developed for a scientific task and demonstrates robustness and generalizability but lacks interpretability in terms of an existing scientific knowledge basis, is this still a useful scientific result?
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 workshop will also feature an expert panel discussion on ``Philosophy of Science in the AI Era" --- focusing on topics such as scientific understanding in the age of extremely complex ML models, automating science via machines, and ML models as source of inspiration for scientific discoveries. Finally, there will be multiple community building activities such as a voluntary mentorship opportunity and round table discussions on curated topics to foster connection building and facilitate knowledge sharing across disciplines, backgrounds, and career stages.
NeurIPS 2022
The Machine Learning and the Physical Sciences 2022 workshop will be held on December 3, 2022 as a part of the 36th annual conference on Neural Information Processing Systems (NeurIPS).
Speakers and panelists
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Call for papers
In this workshop, we aim to bring together physical scientists and machine learning researchers who work at the intersection of these fields – i.e., applying machine learning to problems in the physical sciences (physics, chemistry, mathematics, astronomy, materials science, biophysics, and related sciences) or using physical insights to understand and improve machine learning techniques.
We invite researchers to submit work particularly in the following areas or areas related to them:
ML for Physics: Applications of machine learning to physical sciences including astronomy, astrophysics, cosmology, biophysics, chemistry, climate science, earth science, materials science, mathematics, particle physics, or any related area;
Physics in ML: Strategies for incorporating prior scientific knowledge into machine learning algorithms, as well as applications of physical sciences to understand, model, and improve machine learning techniques;
ML in the scientific process: Machine learning model interpretability for obtaining insights to physical systems; Automating multiple elements of the scientific method for discovery and operations with experiments;
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, probabilistic programming, simulation-based inference, variational inference, causal inference, etc.
We invite authors to follow the guidelines and best practices from the NeurIPS conference.
Contributed Talks
Several accepted submissions will be selected for contributed talks. Contributed talks can be in-person or remote depending on the preference of the presenter.
Posters
Accepted work will be presented as posters during the workshop. At the same time as the in-person poster session, we will also facilitate a virtual poster session in GatherTown. Authors of submitted papers will be able to indicate their preference for an in-person presentation or a virtual presentation. Furthermore, in order to facilitate viewing presentations in different time zones, the authors of each accepted paper will get the opportunity to submit a 5 minute video that summarizes their work.
In case the number of posters that can be presented in-person is limited by the available physical space, a subset of works will be selected to be presented virtually. We will try to keep the authors preference for in-person/virtual poster presentations in mind during this selection. The remaining posters can be presented during the virtual poster session, and through the 5 minutes videos that will be uploaded to the workshop website.
Important note for work that will be/has been published elsewhere
All accepted works will be made available on the workshop website. This does not constitute an archival publication or formal proceedings; authors retain full copyright of their work and are free to publish their extended work in another journal or conference. We allow submission of works that overlap with papers that are under review or have been recently published in a conference or a journal, including physical science journals. However, we do not accept cross-submissions of the same content to multiple workshops at NeurIPS. (Check the list of accepted workshops this year.)
Submission instructions
Submit paper
Submissions should be anonymized short papers (extended abstracts) up to 4 pages in PDF format, typeset using the NeurIPS paper template .
The authors are required to include a short statement (approximately one paragraph) about the potential broader impact of their work, including any ethical aspects and future societal consequences, which may be positive or negative. The broader impact statement should come after the main paper content. The impact statement and references do not count towards the page limit.
The NeurIPS style template includes a paper checklist intended to encourage best practices for responsible machine learning research (see associated guidelines ). Although we require authors to complete the checklist in order to raise awareness of and encourage these practices we expect, given the scope and format of the workshop, that many of the checklist items will not be applicable to the submitted papers. As such, answering "no" or "n/a" to the checklist items will not reflect adversely on submissions and we do not expect authors to further qualify their answers.
Appendices are highly discouraged, and reviewers will not be required to read beyond the first 4 pages and the impact statement.
A workshop-specific modified NeurIPS style file will be provided for the camera-ready versions, after the author notification date.
Workshop organizers retain the right to reject submissions for editorial reasons: for example, any paper surpassing the page limitation or not including the broader impact statement will be desk-rejected.
Submissions will be kept confidential until they are accepted and until authors confirm that they can be included in the workshop. If a submission is not accepted, or withdrawn for any reason, it will be kept confidential and not made public.
Review process
Submissions that follow the submission instructions correctly (i.e., are not rejected due to editorial reasons, such as exceeding the page limit, missing the impact statement, etc,) are sent for peer-review. Below are some of the key points about this process that are shared with the reviewers and authors alike. Authors are expected to consider these in preparation of their submissions and when deciding to apply for the reviewer role.
Papers are 4 pages long. Appendices are accepted but highly discouraged; the reviewers will not be required to read the appendices.
There will be multiple reviewers for each paper.
Reviewers will be able to state their confidence in their review.
We will provide an easy-to-follow template for reviews so that both the pros and the cons of the submission can be highlighted.
Reviewers will select their field of expertise so that each submission has reviewers from multiple fields. During the matching process, the same list of subject fields is used for submissions and reviewer expertise in order to maximize the quality of reviews.
Potential conflicts of interest based on institution and author collaboration are addressed through the CMT review system.
Criteria for a successful submission include: novelty, correctness, relevance to the field, at the intersection of ML and physical sciences, and showing promise for future impact. Negative or null results that add value and insight are welcome.
There will be no rebuttal period. Minor flaws will not be the sole reason to reject a paper. Incomplete works at an advanced progress stage are welcome.
More detailed guidelines for reviewers will follow.
Organizers