Events
DMS Applied and Computational Mathematics Seminar |
| Time: Sep 16, 2022 (02:00 PM) |
| Location: 328 Parker Hall |
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Details: Speaker: Yimin Zhong
Title: How much can one learn a PDE from its solution?
Abstract: In this work, we study a few basic questions for PDE learning from observed solution data. Using various types of PDEs, we show 1) how the approximate dimension (richness) of the data space spanned by all snapshots along a solution trajectory depends on the differential operator and initial data, and 2) identifiability of a differential operator from solution data on local patches. Then we propose a consistent and sparse local regression method (CaSLR) for general PDE identification. Our method is data driven and requires a minimal amount of local measurements in space and time from a single solution trajectory by enforcing global consistency and sparsity.
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