Machine learning methods have had great success in learning complex representations of data that enable novel modeling and data processing approaches in many scientific disciplines. Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets in trillions of sky pixels, to developing solutions to the quantum many-body problem and combinatorial problems, to detecting anomalies in event streams from the Large Hadron Collider, to predicting how extreme weather events will vary with climate change. Tackling a number of associated data-intensive tasks including, but not limited to, segmentation, computer vision, sequence modeling, causal reasoning, generative modeling, and probabilistic inference are critical for furthering scientific discovery in these and many other areas. In addition to using machine learning models for scientific discovery, the ability to interpret what a model has learned is receiving an increasing amount of attention.

In this targeted workshop, we aim to bring together computer scientists, mathematicians and physical scientists who are interested in applying machine learning to various outstanding physical problems including in inverse problems, approximating physical processes, understanding what a learned model represents, and connecting tools and insights from the physical sciences to the study of machine learning models. In particular, the workshop invites researchers to contribute short papers (extended abstracts) that demonstrate cutting-edge progress in the application of machine learning techniques to real-world problems in the physical sciences and/or using physical insights to understand and improve machine learning techniques.

By bringing together machine learning researchers and physical scientists who apply machine learning, we expect to strengthen the 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.

NeurIPS 2020

The Machine Learning and the Physical Sciences 2020 workshop will be held on December 11, 2020 as a part of the 34th Annual Conference on Neural Information Processing Systems. Originally planned to be at the Vancouver Convention Centre, Vancouver, BC, Canada, NeurIPS 2020 and this workshop will take place entirely virtually (online). Please check the main conference website for the latest information.




Steering Committee

Call for papers

We invite researchers to submit work particularly in the following and related areas:

  • Application of machine learning to physical sciences
  • Generative models
  • Likelihood-free inference
  • Variational inference
  • Simulation-based inference
  • Implicit models
  • Probabilistic models
  • Model interpretability
  • Approximate Bayesian computation
  • Strategies for incorporating prior scientific knowledge into machine learning algorithms
  • Experimental design
  • Any other area related to the subject of the workshop

Submissions of completed projects as well as high-quality works in progress are welcome. All accepted short papers (extended abstracts) 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 extended abstracts that overlap with papers that are under review or have been recently published in a conference or a journal. However, we do not accept cross submissions of the same extended abstract to multiple workshops at NeurIPS. Submissions will be kept confidential until they are accepted and 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.

Accepted work will be presented as posters during the workshop. Each accepted work entering the poster sessions would have an accompanying pre-recorded 5-minute video. Please note that at least one coauthor of each accepted paper will be expected to have a NeurIPS conference registration that includes the workshop session and participate in one of the virtual poster sessions.

Submission instructions

Submissions should be anonymized short papers (extended abstracts) up to 4 pages in PDF format, typeset using the NeurIPS style. The authors are required to include a short statement (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 (see the NeurIPS style files for an example). The impact statement and references do not count towards the page limit. Appendices are discouraged, and reviewers are not expected 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.

Submissions page is here.

Submit paper

Important dates

  • Submission deadline: September 25, October 2, October 5, 2020, 23:59 PDT
  • Author notification: October 23, 2020
  • Camera-ready (final) paper deadline: November 23, 2020, 23:59 PDT
  • Workshop: December 11, 2020


NeurIPS conference has three main sessions (Tutorials, Conference, Workshops) to which you can register. You need to be registered to at least the Workshop session in order to be able to attend this workshop. For the latest registration-related information please refer to NeurIPS 2020 website.




Originally planned to be at the Vancouver Convention Centre, Vancouver, BC, Canada, NeurIPS 2020 and this workshop will take place entirely virtually (online). Please check the main conference website for the latest information.