Physics-Informed Machine Learning
The Workshop on Physics-Informed Machine Learning (PIML) was held from 16–18 September 2026 at the Foundation for Research and Technology – Hellas (FORTH) in Heraklion, Crete.
The workshop brought together researchers working at the intersection of machine learning, inverse problems and physics-based modelling, with applications spanning Earth observation, medical imaging and astrophysics.
The event was co-sponsored by the EU TITAN project and the IEEE Geoscience and Remote Sensing Society (IEEE GRSS).
Day 1 — Wednesday 16 September
Day 2 — Thursday 17 September
Day 3 — Friday 18 September
Keynote Speakers
Experts from cosmology, Earth observation, inverse problems, scientific machine learning and medical imaging contributed to the workshop.
Machine learning for cosmology, Bayesian inference, simulation-based inference and generative models.
Artificial intelligence and machine learning for Earth observation and remote sensing.
Machine learning, statistical modelling and physical modelling for cosmological surveys.
Physics-informed machine learning, inverse problems, signal reconstruction and medical imaging.
Remote sensing, Earth observation, neural networks and machine learning for geoscience applications.
Machine learning, inverse problems, uncertainty quantification and scientific data-driven methods.
Workshop Topics
The workshop explored the integration of physical knowledge, scientific modelling and modern machine-learning methods.
Official Workshop Programme
Browse the complete programme for the three-day Physics-Informed Machine Learning Workshop.
PIML 2026 Photo Repository
Browse photographs from each day of the Physics-Informed Machine Learning Workshop at FORTH. Select a workshop day below to switch galleries.
Organizing Committee
TITAN & IEEE GRSS
The PIML Workshop was co-sponsored by the EU TITAN project under Horizon Europe and the IEEE Geoscience and Remote Sensing Society (IEEE GRSS).