SOLIS recovers interpretable physical parameters for nonlinear systems by learning state-conditioned Quasi-LPV neural surrogates without assuming a fixed global equation.
Fourier features let networks learn high frequency functions in low dimensional domains,
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SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
SOLIS recovers interpretable physical parameters for nonlinear systems by learning state-conditioned Quasi-LPV neural surrogates without assuming a fixed global equation.