November 9, 2025Open Access

On Vector Field Reconstruction from Noisy ODE in High Ambient Dimension

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HHH. HenneuseEndangered Language Fund

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Implication

Analysis demonstrates convergence rates dependent on temporal resolution and mass concentration in a noisy ODE framework, indicating effective reconstruction in high-dimensional spaces.

Key Points

Reconstruction of vector fields achieved despite noise in trajectories, ensuring results amidst high dimensionality.
Minimax optimal convergence rates are established, reflecting the influence of temporal resolution and mass concentration.
Estimation combines flow reconstruction and derivative techniques from nonparametric regression for effectiveness.
Effective methods demonstrate computational efficiency in addressing the challenges posed by the curse of dimensionality.

Cite This Study

H. Henneuse (2025) studied this question.

synapsesocial.com/papers/690fdce2f60c54d04ea3831f https://doi.org/10.48550/arxiv.2503.08355

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Implication

Analysis demonstrates convergence rates dependent on temporal resolution and mass concentration in a noisy ODE framework, indicating effective reconstruction in high-dimensional spaces.

On Vector Field Reconstruction from Noisy ODE in High Ambient Dimension

Discussion

Member takes

Implication

Key Points

Cite This Study

On Vector Field Reconstruction from Noisy ODE in High Ambient Dimension

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider