<ul><li>Researchers have developed a computational technique for modeling the evolution of dynamical systems in a reduced basis.</li><li>The focus of the study is on modeling partially-observed partial differential equations (PDEs) on high-dimensional non-uniform grids.</li><li>The technique addresses the limitations of previous work by considering noisy and limited data, simulating real-world data collection scenarios.</li><li>By leveraging recent advancements in PDE modeling, the researchers propose a neural network structure that is suitable for modeling PDEs with noisy and limited data.</li></ul>

Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data

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