# Call for Papers

We invite submissions to the ICLR 2023 workshop on Physics for Machine Learning. The total length of the initial (and final) submission must not exceed **4 pages**, with unlimited additional pages for citations.
Authors may include an appendix after the references. However, reviewers are not required to read the appendix.
Submissions should be a single file in `.pdf`

format using this style file. The review process is
*double-blind*, so please ensure that all papers are appropriately
anonymized. We reserve the right to desk-reject improperly-anonymized
papers.

This workshop is **non-archival**; even though all accepted papers will be
available on OpenReview, there are *no* formally-published proceedings.

Submission link |
OpenReview |

Submission opens |
1 January 2023 |

Submission deadline |
8 February 2023 (AoE) |

Final decisions |
3 March 2023 |

# List of topics

We invite all submissions on using physics for machine learning methods.
A list of exemplary topics can be found below. Please note that this list is *non-exhaustive*.
If you are not sure if your topic is suitable for the workshop, please feel free to contact any of the organizers.

- Physics-inspired machine learning; in particular for
- Graph representation learning
- Sequence modeling (e.g. Transformers, RNNs)
- Generative modeling (e.g. diffusion models, score-based SDEs, normalizing flows)
- Neural ODEs (e.g. NCDEs, CNFs)

- Equivariant neural networks
- Physics-based optimization
- Machine learning methods with a physics-based inductive bias, for instance applied to
- Molecular simulations
- Fluid dynamics
- Astrophysics
- Particle physics
- Multi-scale problems (e.g. in multi-physics)

- Physics-based symbolic regression
- Dynamical systems reconstruction with physics-based inductive bias