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

arXiv: https://arxiv.org/abs/2407.10854 · HTML: https://mlsanity.com/paper/2407.10854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2024.

## Abstract (arXiv)

We present a computational technique for modeling the evolution of dynamical systems in a reduced basis, with a focus on the challenging problem of modeling partially-observed partial differential equations (PDEs) on high-dimensional non-uniform grids. We address limitations of previous work on data-driven flow map learning in the sense that we focus on noisy and limited data to move toward data collection scenarios in real-world applications. Leveraging recent work on modeling PDEs in modal and nodal spaces, we present a neural network structure that is suitable for PDE modeling with noisy and limited data available only on a subset of the state variables or computational domain. In particular, spatial grid-point measurements are reduced using a learned linear transformation, after which the dynamics are learned in this reduced basis before being transformed back out to the nodal space. This approach yields a drastically reduced parameterization of the neural network compared with previous flow map models for nodal space learning. This allows for rapid high-resolution simulations, enabled by smaller training data sets and reduced training times.