Conferences topics
Topics of interests include but are not limited to:
- Geometric Learning and Differential Invariants on Homogeneous Spaces
- Statistical Manifolds and Hessian information geometry
- Renyi Entropy & Information
- Geometric Foliation Structures of Dissipation and Machine Learning
- Geometric Structures of Quantum Information & Processing
- Applied Geometric Learning
- Probability, Information and Topology (fundamentals & applications)
- Divergences in Statistics and Machine Learning
- Geometric Statistics
- Geometric Methods in Hybrid Classical/Quantum Systems
- Computational Information Geometry and Divergences
- Geometric Methods in Thermodynamics
- The Geometry of Classical & Quantum States
- Geometric Mechanics
- Geometric Green & Quantum Machine Learning
- Stochastic Geometric Dynamics
- New trends in Nonholonomic Systems
- Learning of Dynamic Processes
- Neurogeometry
- PINN (Physics-Informed Neural Network) with Geometric Structures
- Lie Groups in Machine Learning
- Information Geometry, Toric Manifold (Delzant Theory)
- A symplectic approach to differential equations
- Lie Group Based Method in Robotics & Kalman Filters
- Geometric and Analytical Aspects of Quantization and Non-Commutative Harmonic Analysis on Lie Groups
- Probability and Statistics on manifolds
- Deep learning: Methods, Analysis and Applications to Mechanical Systems
- Integrable Systems and Information Geometry (From Classical to Quantum)
- Computing Geometry & Algebraic Statistics