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Out-of-Domain Generalization in Dynamical Systems Reconstruction In this work, we provide a formal framework that addresses generalization in DSR We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning
Out-of-Domain Generalization in Dynamical Systems Reconstruction - PMLR We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
Out-of-Domain Generalization in Dynamical Systems Reconstruction We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
Out-of-Domain Generalization in Dynamical Systems Reconstruction We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
Out-of-Domain Generalization in Dynamical Systems . . . We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning We introduce mathematical notions based on topological concepts and ergodic theory to formalize the idea of learnability of a DSR model
Out-of-Domain Generalization in Dynamical Systems Reconstruction Like any good scientific theory, a proper DS model inferred from data should be able to generalize to novel domains (dynamical regimes) not observed during training Here we develop a principled mathematical framework for out-of-domain (OOD) generalization (OODG) in DSR
PINN-based joint identification and low-dimensional dynamical modeling . . . The GMM provides a low-dimensional dynamical framework with strong physical interpretability, while neural networks offer flexibility to approximate complex nonlinear joint behaviors Studies have shown that this hybrid approach improves generalization capabilities compared to fully data-driven methods [73]