A discrete-time energy-conserving neural map with FDT-derived causal regularization reproduces stationary statistics and forced responses of CdV and Lorenz-96 turbulence from unperturbed data alone.
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Adding a weak-form penalty to the standard pointwise loss makes Neural ODE training robust to observation noise and preserves long-term invariant statistics on chaotic benchmarks and ERA5 climate data.
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Physics constraints and response validation in discrete-time reduced-order modeling: from idealized turbulent systems to climate dynamics
A discrete-time energy-conserving neural map with FDT-derived causal regularization reproduces stationary statistics and forced responses of CdV and Lorenz-96 turbulence from unperturbed data alone.
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A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
Adding a weak-form penalty to the standard pointwise loss makes Neural ODE training robust to observation noise and preserves long-term invariant statistics on chaotic benchmarks and ERA5 climate data.