MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
Neural structure learning with stochastic differential equations
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Sparse linear ODEs are unidentifiable with positive probability from single trajectories in relevant sparsity regimes, supported by lower bounds and empirical evidence that estimation methods fail to recover unique parameters.
citing papers explorer
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Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems
MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
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Identifiability Challenges in Sparse Linear Ordinary Differential Equations
Sparse linear ODEs are unidentifiable with positive probability from single trajectories in relevant sparsity regimes, supported by lower bounds and empirical evidence that estimation methods fail to recover unique parameters.