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Scientific machine learning, physics-informed models, and data-driven ML for solving real-world problems.

This three-day physical workshop will feature morning and afternoon talks each day, along with dedicated sessions for focused discussions.

Focus areas:

Geometric deep learning
Numerical-method–inspired network architectures
Analysis of stability, robustness, and generalization in physics-guided ML models
Physics-informed, constraint-aware, and structure-preserving learning methods
Data-efficient learning for physical systems
Operator learning and ML approaches for PDEs and dynamical systems
Neurosymbolic approaches that combine physical rules or symbolic structure with learned models

Confirmed speakers:

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