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Predicting the evolution of complex systems governed by partial differential equations remains challenging, especially for nonlinear, chaotic behaviors. This study introduces Koopman-inspired Fourier neural operators and convolutional neural networks to learn solution advancement operators for flame front instabilities. By transforming data into a high-dimensional latent space, these models achiev
