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Physics-Informed Neural Operator (PINO) dataset
The dataset used in the paper for training and testing the Physics-Informed Neural Operator (PINO) model. -
SNAPE: Theory-guided learning of PDEs from data
The dataset used in this paper is a collection of measured responses of various physical processes, including the wave equation, chaotic response of forced Duffing oscillator,... -
DGGO dataset
The dataset used in this paper for testing the DGGO for solving the 1D Burgers equation, 1D time-dependent wave advection equation, 2D Darcy flow equation, and 2D time-dependent... -
2D KS System with Spatial Inhomogeneities
The dataset used in the paper is a 2D KS system with spatial inhomogeneities. The system is simulated on a grid of size 256x256. -
2D KS System
The dataset used in the paper is a 2D KS system with periodic boundary conditions. The system is simulated on a grid of size 256x256. -
Sliding-Window Approach for PDEs
The dataset used in the paper is a sliding-window approach with an extent of ℓ = 1 and a stride of s = 2. The dataset is a 256x256 grid with 512 nodes, and the state function is... -
Parametric diffusion equation
The dataset used in this paper is a collection of parametrized PDEs, specifically the parametric diffusion equation, with affine parametric dependence on the diffusion term. -
Predictions based on pixel data: Insights from PDEs and finite differences
The dataset used in this paper is a set of space-time observations of PDE solutions, generated using finite element simulations. -
Implicit Multigrid-Augmented DL for the Helmholtz Equation
The dataset used in this paper is a collection of slowness models for the Helmholtz equation, generated from the CIFAR-10, OpenFWI Style-A, and STL-10 datasets. -
Heat Equation, Burgers Equation, and Kuramoto-Sivashinsky Equation Datasets
The dataset used for training and testing the neural network for linearizing the heat equation, Burgers equation, and Kuramoto-Sivashinsky equation. -
Continuous PDE Dynamics Forecasting dataset
A dataset for continuous PDE dynamics forecasting, including the Navier-Stokes equation. -
Message Passing Neural PDE Solvers dataset
A dataset for message passing neural PDE solvers, including the Navier-Stokes equation. -
D1Q3 Lattice Boltzmann simulations of the coupled FitzHugh-Nagumo PDEs
The dataset generated by D1Q3 Lattice Boltzmann simulations of the coupled FitzHugh-Nagumo PDEs. -
Cable equation
Experimental data set of the cable equation ut = uxx/(RlC) + u/(RmC) with C the capacitance, Rl the longitudinal resistance and Rm the parallel resistance of the circuit. -
2D advection-diffusion
Experimental data set of the 2D advection-diffusion process described by ut = −∇ · (−D∇u + (cid:126)v). -
Burgers equation
Synthetic data set of the Burgers equation ut = νuxx − uux, with a delta peak initial condition u(x, t = 0) = Aδ(x) and domain t ∈ [0.1, 1.1], x ∈ [−3, 4]. -
Helmholtz Equation Dataset
The dataset used in this paper is a collection of random residual and error vectors, as well as a set of heterogeneous slowness models. -
2-D Navier-Stokes vorticity-transport equation
2-D Navier-Stokes vorticity-transport equation -
2-D Poisson equation
2-D Poisson equation -
Learning black- and gray-box chemotactic PDEs/closures from agent-based Monte...
A machine-learning framework for the numerical solution of the inverse problem in chemotaxis.
