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Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Yuan Yin · Matthieu Kirchmeyer · Jean-Yves Franceschi · alain rakotomamonjy · patrick gallinari
Keywords: [ Machine Learning for Sciences ] [ implicit neural representations ] [ dynamical systems ] [ pdes ] [ spatiotemporal forecasting ] [ partial differential equations ] [ generalization ] [ INRs ] [ continuous models ] [ physics ]
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather prediction where flexible extrapolation at arbitrary spatiotemporal locations is required. We address this problem by introducing a new data-driven approach, DINo, that models a PDE's flow with continuous-time dynamics of spatially continuous functions. This is achieved by embedding spatial observations independently of their discretization via Implicit Neural Representations in a small latent space temporally driven by a learned ODE. This separate and flexible treatment of time and space makes DINo the first data-driven model to combine the following advantages. It extrapolates at arbitrary spatial and temporal locations; it can learn from sparse irregular grids or manifolds; at test time, it generalizes to new grids or resolutions. DINo outperforms alternative neural PDE forecasters in a variety of challenging generalization scenarios on representative PDE systems.