Keywords: Data-driven equation discovery, dynamical system modeling, physics-encoded learning
Abstract: There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. Although past research attempts have achieved great success in data-driven PDE discovery, the robustness of the existing methods cannot be guaranteed when dealing with low-quality measurement data. To overcome this challenge, we propose a novel physics-encoded discrete learning framework for discovering spatiotemporal PDEs from scarce and noisy data. The general idea is to (1) firstly introduce a novel deep convolutional-recurrent networks, which can encode prior physics knowledge (e.g., known terms, assumed PDE structure, initial/boundary conditions, etc.) while remaining flexible on representation capability, to accurately reconstruct high-fidelity data, and (2) then perform sparse regression with the reconstructed data to identify the analytical form of the governing PDEs. We validate our proposed framework on three high-dimensional PDE systems. The effectiveness and superiority of the proposed method over baselines are demonstrated.
One-sentence Summary: This work seeks to solve the data-driven governing equation discovery problem with a novel physics-encoded learning framework.