Simultaneous learning of dynamics and coordinates, with examples in fluid dynamics (Keynote Lecture)
Steven Brunton

Bayesian inference for the learning of reduced-state dynamics
Mengwu Guo

Model reduction of parameterized PDEs using deep bases
Eric Parish, Yukiko Shimizu and Kookjin Lee

Deep learning of dynamical systems using geometry and thermodynamics
Quercus Hernández, Alberto Badías, David González, Francisco Chinesta and Elías Cueto

Physics-constrained deep learning-based reduced order models for parametrized PDEs
Stefania Fresca, Federico Fatone, Mengwu Guo and Andrea Manzoni

On the employ of inductive biases for the development of learned simulators
Quercus Hernandez, Alberto Badias, Francisco Chinesta and Elias Cueto

Physics-informed neural networks as reduced simulation models for bioreactor and crystallisation modeling
Daniel Wolff, Konstantin Key, Eric von Lieres and Stefanie Elgeti

Deep-HyROMnet: a deep learning-based operator approximation for model order reduction in structural mechanics
Ludovica Cicci, Stefania Fresca and Andrea Manzoni

Active learning for non-intrusive model reduction of parametric dynamical systems
Harshit Kapadia, Lihong Feng and Peter Benner

Machine learning accelerated dynamic analysis of stochastic nonlinear structures
Stefanos Nikolopoulos, Ioannis Kalogeris and Vissarion Papadopoulos