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Presented is a data-driven Machine Learning (ML) framework for the identification and modeling of traveling wave spatiotemporal dynamics. The presented framework is based on the steadily-propagating traveling wave ansatz, u(x,t) = U(ξ=x - ct + a). For known evolution equations, this coordinate transformation reduces governing partial differential equations (PDEs) to a set of coupled ordinary differential equations (ODEs) in the traveling wave coordinate ξ. Although traveling waves are readilyarXiv:2101.02122v1 fatcat:oqgsyjmqyffm3f3uqdzd6upxxe