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
This workshop is non-archival; even though all accepted papers will be available on OpenReview, there are no formally-published proceedings.
|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
- Particle physics
- Multi-scale problems (e.g. in multi-physics)
- Physics-based symbolic regression
- Dynamical systems reconstruction with physics-based inductive bias