Pages that link to "Item:Q2112549"
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The following pages link to Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems (Q2112549):
Displaying 8 items.
- Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow (Q6080313) (← links)
- Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data (Q6086911) (← links)
- A multifidelity deep operator network approach to closure for multiscale systems (Q6116145) (← links)
- Incremental neural controlled differential equations for modeling of path-dependent material behavior (Q6125468) (← links)
- Learning subgrid-scale models with neural ordinary differential equations (Q6160037) (← links)
- Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches (Q6554429) (← links)
- Divide and conquer: learning chaotic dynamical systems with multistep penalty neural ordinary differential equations (Q6641946) (← links)
- Neural dynamical operator: continuous spatial-temporal model with gradient-based and derivative-free optimization methods (Q6648386) (← links)
