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Examples & tutorials, in pictures.

Every card links to a runnable Jupyter notebook in the documentation. Click through to see the full code and explanations.

1D viscous Burger equation
Example

1D viscous Burger equation

Solves the viscous Burgers advection equation in 1D using a PINN.

Inhomogeneous Helmholtz equation
Example

Inhomogeneous Helmholtz equation

Solves an inhomogeneous Helmholtz equation using a PINN and a MLP with multi-scale Fourier features.

Linearized Euler system
Example

Linearized Euler system

Solves the linearized Euler system using a PINN.

Grad div system 2D
Example

Grad div system 2D

Solves a 2D grad div system with PINNs.

Div A grad u system 2D
Example

Div A grad u system 2D

Solves a 2D div A grad u system with the deep Ritz method.

Navier-Stokes system of equations
Example

Navier-Stokes system of equations

Solves a 2D Navier-Stokes system with PINNs.

2D Laplacian on a disk
Example

2D Laplacian on a disk

Solves a 2D Laplacian on a disk with the deep Ritz method.

Pendulum flow
Example

Pendulum flow

Learns the flow of a pendulum ODE with different training strategies.

Kinetic Vlasov equation
Example

Kinetic Vlasov equation

Solves a kinetic Vlasov equation in 1D with the neural semi-Lagrangian approach.

Rotating transport equation
Example

Rotating transport equation

Solves a rotating transport equation in 2D with the neural Galerkin approach.

2D heat equation
Example

2D heat equation

Solves a 2D heat equation with discrete PINNs using an implicit scheme.

Transport equation in a cylinder
Example

Transport equation in a cylinder

Solves a transport equation in a cylinder with the neural semi-Lagrangian approach.

Advection of a 3D level-set function
Example

Advection of a 3D level-set function

Solves the advection of a 3D level-set function with the neural semi-Lagrangian approach.

Learning a Signed Distance Function
Example

Learning a Signed Distance Function

Learns the Signed Distance Function of a shape given as a file of points on its contour.

2D Laplacian problems
Tutorial · Torch

2D Laplacian problems

Solve 2D Laplacian problems with Dirichlet boundary conditions.

Natural gradient preconditioners
Tutorial · Torch

Natural gradient preconditioners

Setup an equation for applying preconditioners.

Defining domains
Tutorial · Torch

Defining domains

Learn how to define and work with computational domains.

Defining models
Tutorial · Torch

Defining models

Learn how to define physical models and PDEs in Scimba for different problem types.

Projection of functions
Tutorial · Torch

Projection of functions

Learn how to approximate a function given as an evaluation procedure with a network-based approximation space.

Saving and loading PINNs
Tutorial · Torch

Saving and loading PINNs

Save and load PINNs.

Strong boundary conditions
Tutorial · Torch

Strong boundary conditions

Implementation of strong boundary conditions in 2D problems.

Mixed boundary conditions
Tutorial · Torch

Mixed boundary conditions

Handling mixed boundary conditions.

Time discrete methods
Tutorial · Torch

Time discrete methods

Explore time discretization methods for temporal PDEs.

Scimba optimizers
Tutorial · Torch

Scimba optimizers

Scimba optimizers how-to.

PINN's losses
Tutorial · Torch

PINN's losses

How to define and tune losses and loss weights.

Basics of scimba_jax
Tutorial · Jax

Basics of scimba_jax

Solve pre-defined stationary and time-dependent models.

Strong boundary conditions
Tutorial · Jax

Strong boundary conditions

Implementation of strong boundary and initial conditions.

Domains and samplers
Tutorial · Jax

Domains and samplers

How to define domains and samplers.

Custom physical models
Tutorial · Jax

Custom physical models

How to define custom physical models.

Scimba

A Python library implementing Scientific Machine Learning methods for PDE problems, with PyTorch and JAX backends. Developed at IRMA (Inria / CNRS / Université de Strasbourg).

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