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What is Learning Nonlinear PDEs from Small Data about?

This paper introduces hidden physics models, a new approach for learning nonlinear partial differential equations (PDEs) from small datasets by leveraging underlying physical laws. The methodology utilizes Gaussian processes for probabilistic inference, allowing for effective system identification and data-driven discovery across various scientific domains, including fluid dynamics and quantum mechanics. The authors demonstrate the effectiveness of their framework through canonical problems, highlighting it

Author

veiqc

Language

EN