1D and 2D PDEs Dataset (Masked Autoencoders are PDE Learners)

Datasets Data was generated according to parameters detailed in the paper using the code below. Message Passing Neural PDE Solvers (https://github.com/brandstetter-johannes/MP-Neural-PDE-Solvers?tab=readme-ov-file) - 1D KdV Burgers equation - 1D Heat equation, Periodic BCs - 1D inviscid Burgers equation - 1D Wave equation Lie Point Symmetry Data Augmentation for Neural PDE Solvers (https://github.com/brandstetter-johannes/LPSDA) - 1D KS Equation Fourier Neural Operator for Parametric Partial Differential Equations (https://github.com/khassibi/fourier-neural-operator) (Update: Repo no longer exists) - 2D Incompressible NS Towards multi-spatiotemporal-scale generalized PDE modeling (https://huggingface.co/datasets/pdearena/NavierStokes-2D-conditoned) - 2D Smoke Buoyancy Masked Autoencoders are PDE Learners (https://github.com/anthonyzhou-1/mae-pdes) - 2D Heat, Adv, Burgers equations - 1D Advection - 1D Heat (Requires a working FEniCS installation: https://fenicsproject.org/download/archive/) Organization Data is organized into the following structure: Split [train/valid/test] u : nodal values of the PDE solution, in shape [num_samples, temporal_resolution, spatial_resolution] x : coordinates of the spatial domain, in shape [spatial_resolution] t : timesteps of the PDE solution, in shape [temporal_resolution] coefficients [alpha, beta, gamma, etc.]: coefficients of the solved PDE solution, in shape [num_samples, coord_dim]