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Found 6 papers, 4 papers with code
DynGMA: a robust approach for learning stochastic differential equations from data
Benefiting from the robust density approximation, our method exhibits superior accuracy compared to baseline methods in learning the fully unknown drift and diffusion functions and computing the invariant distribution from trajectory data.
Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets
We thus formulate an adaptive algorithm which monitors the level of error and adapts the number of (unrolled) implicit solution iterations during the training process, so that the error of the unrolled approximation is less than the current learning loss.
On Numerical Integration in Neural Ordinary Differential Equations
The combination of ordinary differential equations and neural networks, i. e., neural ordinary differential equations (Neural ODE), has been widely studied from various angles.
VPNets: Volume-preserving neural networks for learning source-free dynamics
We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data.
Approximation capabilities of measure-preserving neural networks
Measure-preserving neural networks are well-developed invertible models, however, their approximation capabilities remain unexplored.
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems
We propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules.
