Pages that link to "Item:Q4646127"
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The following pages link to Reconstruction of normal forms by learning informed observation geometries from data (Q4646127):
Displaying 13 items.
- Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach (Q2149520) (← links)
- Spatiotemporal pattern extraction by spectral analysis of vector-valued observables (Q2327837) (← links)
- Manifold learning for organizing unstructured sets of process observations (Q3303828) (← links)
- Model selection for hybrid dynamical systems via sparse regression (Q4973922) (← links)
- Kernel-based parameter estimation of dynamical systems with unknown observation functions (Q4989104) (← links)
- Detecting the maximum likelihood transition path from data of stochastic dynamical systems (Q5140892) (← links)
- (Q5149011) (← links)
- SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics (Q5161113) (← links)
- Parsimony as the ultimate regularizer for physics-informed machine learning (Q6117148) (← links)
- Data-driven reduced order models using invariant foliations, manifolds and autoencoders (Q6168858) (← links)
- Identifying stochastic governing equations from data of the most probable transition trajectories (Q6191971) (← links)
- Maximally predictive states: from partial observations to long timescales (Q6572694) (← links)
- Tipping points of evolving epidemiological networks: machine learning-assisted, data-driven effective modeling (Q6592553) (← links)
