Physics-Informed Machine Learning - PIML Workshop

Physics-Informed Machine Learning - PIML Workshop

PIML Workshop Coming to ICS-FORTH in September 2026

The Workshop on Physics-Informed Machine Learning will be held from 16–18 September 2026 at the Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece. The workshop brings together leading researchers working at the intersection of machine learning, inverse problems, and physics-based modelling, with applications in Earth observation, medical imaging, and astrophysics. The event is co-sponsored by the EU TITAN project and the IEEE Geoscience and Remote Sensing Society (IEEE GRSS).

Registration Form

Registration for the PIML Workshop is now open. Please complete the form below to register your participation.

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Optional Abstract Submission

Participants who wish to present their work may optionally submit an extended abstract through the form below.

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Description

Physics-informed machine learning represents a rapidly evolving frontier at the intersection of scientific modelling and data-driven methods. By embedding physical laws, governing equations, and domain knowledge into learning architectures, PIML methods offer principled approaches to inverse problems with limited or noisy observations.

Topics of interest include:

  • Physics-informed and hybrid AI models for inverse problems
  • Deep unrolling and algorithm unfolding for signal reconstruction
  • Neural operators and surrogate modelling for PDEs
  • Generative models and learned priors for imaging and sensing
  • Bayesian inference and uncertainty quantification in physics-constrained learning
  • Applications in Earth observation, medical imaging, and astrophysics

Call for Contributions & Submission Guidelines

Dates: 16–18 September 2026
Location: FORTH, Heraklion, Crete, Greece

Participation in the workshop does not require an abstract submission. Researchers who wish to present their work are invited to optionally submit an extended abstract (500 words max) presenting original or recently published work related to the workshop topics.

Organizing Committee

Keynote Speakers

  • Justine Zeghal
    Postdoctoral Fellow, Université de Montréal / Ciela Institute / Mila. Her research focuses on machine learning methods for cosmology, Bayesian inference, simulation-based inference, and generative models.
  • Ioannis Papoutsis
    Head of Orion Lab; Assistant Professor of Artificial Intelligence for Earth Observation at the National Technical University of Athens (NTUA); Adjunct Researcher at the National Observatory of Athens (NOA).
  • François Lanusse
    Cosmologist and Deep Learning Researcher at CNRS, member of the CosmoStat Laboratory. His work combines machine learning, statistical modelling, and physical modelling for cosmological surveys.
  • Florent Sureau
    Researcher at CEA-SHFJ, UMR BioMaps. His work focuses on physics-informed machine learning, inverse problems, signal reconstruction, and applications in medical imaging.
  • Fabio Del Frate
    Professor at the University of Rome Tor Vergata. His work focuses on remote sensing, Earth observation, neural networks, and machine learning methods for geoscience applications.
  • Cail Daley
    Researcher at Université Paris Cité. His work focuses on machine learning, inverse problems, uncertainty quantification, and scientific applications of data-driven methods.
    cail.daley@u-paris.fr

Important Dates

  • Registration opens: Open now
  • Abstract Submission: Optional
  • Abstract Submission Deadline: 30 June 2026
  • Notification of acceptance: 5 July 2026
  • Conference Registration Deadline: 10 September 2026

Sponsors

This workshop is co-sponsored by the EU TITAN project (Horizon Europe) and the IEEE Geoscience and Remote Sensing Society (IEEE GRSS).