Dissipation enhances neural surrogate learnability of open quantum dynamics in spin chains at intermediate sizes via contraction, but fidelity metrics must separate genuine dynamics from steady-state trivialization.
Dissecting neural odes
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FNODE projects Neural ODE dynamics into the frequency domain via FFT and reports better generalization and convergence stability than GRUs, LSTMs, and ANODE on Lotka-Volterra, forced Duffing, Van der Pol, and Lorenz systems.
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When does dissipation help neural surrogates learn open quantum dynamics?
Dissipation enhances neural surrogate learnability of open quantum dynamics in spin chains at intermediate sizes via contraction, but fidelity metrics must separate genuine dynamics from steady-state trivialization.
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Frequency-Domain Neural ODEs for Modeling Non-Linear Dynamical Systems
FNODE projects Neural ODE dynamics into the frequency domain via FFT and reports better generalization and convergence stability than GRUs, LSTMs, and ANODE on Lotka-Volterra, forced Duffing, Van der Pol, and Lorenz systems.