The ML-DE Workshop on “Machine Learning Meets Differential Equations: From Theory to Applications”, to be held in conjunction with ECAI 2024, welcomes submissions that blend the latest advancements in Machine Learning (ML) and Differential Equations (DE). Our goal is to foster an environment where novel insights, methodologies, or applications at this interdisciplinary nexus can be shared and discussed.

Download and share the Call for Papers as a PDF document here.

Publication Types

We are excited to invite submissions in a variety of formats, catering to different stages of research and modes of presentation:

  • Full-Length Papers: Maximum of 8 pages, excluding references and supplementary material, for paper presentations.
  • Extended Abstracts: Limited to 2 pages, including references, designed for poster sessions and brief elevator pitches (approximately 5 minutes). This format provides a snapshot of your research, perfect for generating interest and discussion.
  • Presentation Only: Authors of papers recently published in top-tier conferences and journals (AAAI, JMLR, JAIR, MLJ, PAMI, IJCAI, NeurIPS, ICLR, AISTATS, ICML) are encouraged to submit a 2-page extended abstract, including references, for presentation. Please indicate the original publication venue in your submission form.
  • Reproducibility Track: Contributions that enhance the reproducibility of research findings are crucial. We invite interactive tutorials, demos, libraries, packages or datasets (e.g. Jupyter notebooks) and their respective 2-page extended abstracts. This track emphasizes the practical application and implementation of research, facilitating a deeper understanding and broader use of ML-DE techniques. Demo code (e.g. Jupyter notebooks etc.) will be published jointly at our github together with a link to the paper.

Highlight

Full-Length Papers will be published collectively in a volume by the Proceedings of Machine Learning Research (PMLR). This collaboration aims to enhance accessibility to emerging topics and expand the field’s network. Authors can opt-in or opt-out.

Topics of Interest

We encourage submissions across a broad range of topics, including, but not limited to:

  • Embedding differential equations into machine learning (Neural ODEs, normalising flows, …).
  • Solving differential equations using machine learning (PINNs, Neural Operators, …).
  • Machine Learning-augmented numerical methods for solving differential equations (hybrid solvers, …).
  • Analysis of numerical methods for incorporating differential equations’ solvers into machine learning algorithms (trade-offs, benchmarks, …).
  • Incorporation of expert-knowledge given by differential equations into machine learning algorithms (physics-inspired machine learning, …).
  • Applications of the above to modelling/predicting real-world systems in science and engineering (finance, biology, physics, chemistry, engineering, …).
  • Use of machine learning to model systems described by differential equations (finance, biology, physics, chemistry, engineering, …).
  • Approaches to extract physical knowledge out of learned differential equations for explainable AI (SINDy, …).
  • Computational efficiency of DE solvers involved in ML algorithms (ODE solvers, …).

Submission Guidelines

Join Us in Person!

We warmly invite you to present your work in person, fostering a vibrant exchange of ideas. Note: We reserve the right to withdraw papers from our program and the PMLR for those that do not intend to present in person.

Important Dates

  • Submission Deadline: 15th May 2024 23:59 CEST 1st of July 2024 23:59 CEST
  • Notification of Acceptance: 15th of August 2024
  • Workshop Date: Sunday, 20th of October, Full Day

For more details and submission instructions, please contact us at MLDEWorkshopECAI24@hsu-hh.de.

We eagerly await your submissions and hope to see you in Santiago de Compostela for ECAI’s 50th anniversary!