A Koopman-Chebyshev framework identifies the governing PDE among four candidate linear equations from noiseless observations, using a data-projected discrepancy instead of eigenvalue comparisons.
Reduced Order Data-Driven Twin Models for Nonlinear PDEs by Randomized Koopman Orthogonal Decomposition and Explainable Deep Learning,
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Numerical Spectrum Linking: Identification of Governing PDE via Koopman-Chebyshev Approximation with Resampling
A Koopman-Chebyshev framework identifies the governing PDE among four candidate linear equations from noiseless observations, using a data-projected discrepancy instead of eigenvalue comparisons.