A class of physics-informed spatio-temporal models has recently been proposed for modeling spatio-temporal processes governed by advection-diffusion equations. The central idea is to approximate the process by a truncated Fourier series and let the governing physics determine the dynamics of the spectral coefficients. However, because many spatio-temporal processes in real applications are non-periodic with boundary discontinuities, the well-known Gibbs phenomenon and ripple artifact almost always exist in the outputs generated by such models due to truncation of the Fourier series. Hence, the key contribution of this paper is to propose a physics-informed spatio-temporal modeling approach that significantly suppresses the Gibbs phenomenon when modeling spatio-temporal advection-diffusion processes. The proposed approach starts with a data flipping procedure for the process respectively along the horizontal and vertical directions (as if we were unfolding a piece of paper that has been folded twice along the two directions). Because the flipped process becomes spatially periodic and has a complete waveform without any boundary discontinuities, the Gibbs phenomenon disappears even if the Fourier series is truncated. Then, for the flipped process and given the Partial Differential Equation (PDE) that governs the process, this paper extends an existing PDE-based spatio-temporal model by obtaining the new temporal dynamics of the spectral coefficients, while maintaining the physical interpretation of the flipped process. Numerical investigations based on a real dataset have been performed to demonstrate the advantages of the proposed approach. It is found that the proposed approach effectively suppresses the Gibbs Phenomenon and significantly reduces the ripple artifact in modeling spatio-temporal advection-diffusion processes. Computer code is available on GitHub.

Gibbs Phenomenon Suppression in PDE-Based Statistical Spatio-Temporal Models

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Gibbs Phenomenon Suppression in PDE-Based Statistical Spatio-Temporal Models