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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. -
4D-ODE dataset
The dataset used for the 4D-ODE example, containing 10,000 data points with 6 input features and 4 output features. -
1D-ODE dataset
The dataset used for the 1D-ODE example, containing 200 data points with 2 input features and 2 output features. -
3D Elliptic Interface Problem
The dataset used in the paper is a 3D elliptic interface problem with discontinuous coefficients and/or interfacial jumps. -
2D Elliptic Interface Problem
The dataset used in the paper is a 2D elliptic interface problem with discontinuous coefficients and/or interfacial jumps. -
1D Elliptic Interface Problem
The dataset used in the paper is a 1D elliptic interface problem with discontinuous coefficients and/or interfacial jumps. -
Elliptic Interface Problem
The dataset used in the paper is a model elliptic interface problem with discontinuous coefficients and/or interfacial jumps. -
One-Shot Transfer Learning of Physics-Informed Neural Networks
The dataset used in this paper for one-shot transfer learning of physics-informed neural networks. -
Monte Carlo Physics-Informed Neural Networks
A deep learning approach for solving forward and inverse problems involving fractional partial differential equations. -
Allen-Cahn and Helmholtz datasets
The dataset used in this paper for the 1D Allen-Cahn equation and the 2D Helmholtz equation. -
Functional Priors and Posteriors from Data and Physics
The dataset used in this paper is a collection of historical data for learning functional priors and posteriors from data and physics. -
Inverse resolution of spatially varying diffusion coefficient using Physics-I...
The dataset used in the paper is a collection of spatio-temporal information of the diffused passive scalar, with spatially varying diffusion coefficient. -
Navier-Stokes Equations Dataset
The dataset used in this paper for the Navier-Stokes equations problem. -
Heat Equation Dataset
The dataset used in this paper for the heat equation problem. -
Design of Turing Systems with Physics-Informed Neural Networks
Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial... -
PI-VAE dataset
The dataset used in this paper is a collection of stochastic processes, including k(x, ω), f (x, ω), and b(x, ω), which are used to solve stochastic differential equations... -
Scientific Machine Learning through Physics-Informed Neural Networks: Where we...
Physics-Informed Neural Networks (PINNs) are a scientific machine learning technique used to solve problems involving Partial Differential Equations (PDEs). -
Physics-informed neural networks for heat transfer problems
Physics-informed neural networks for heat transfer problems. -
PIANO dataset
The dataset used in this paper is a collection of PDE series with varying physical mechanisms.
