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Examples and tutorials

Each card links to a Jupyter notebook in the documentation, which contains the full code and explanations.

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.

Mixed Boundary Conditions
Tutorial · Jax

Mixed Boundary Conditions

How to apply mixed boundary conditions.

Domains and Samplers
Tutorial · Jax

Domains and Samplers

How to define domains and samplers.

Save and load trained models
Tutorial · Jax

Save and load trained models

How to save and load objects.

Custom Physical models
Tutorial · Jax

Custom Physical models

How to define custom physical models.

2D Laplacian Problems
Tutorial · Torch

2D Laplacian Problems

Solve 2D Laplacian problems with Dirichlet boundary conditions.

Natural Gradients Preconditioners
Tutorial · Torch

Natural Gradients 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 losses weights.

1D viscous Burger equation
Example · Torch

1D viscous Burger equation

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

Inhomogeneous Helmholtz equation
Example · Torch

Inhomogeneous Helmholtz equation

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

Linearized Euler system
Example · Torch

Linearized Euler system

Solves the linearized Euler system using a PINN.

Grad div system 2D
Example · Torch

Grad div system 2D

Solves a 2D grad div system with PINNs.

Div A grad u system 2D
Example · Torch

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 · Torch

Navier Stokes system of equations

Solves a 2D Navier Stokes system with PINNs.

2D laplacian disk
Example · Torch

2D laplacian disk

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

Pendulum flow
Example · Torch

Pendulum flow

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

Kinetic Vlasov equation
Example · Torch

Kinetic Vlasov equation

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

Rotating transport equation
Example · Torch

Rotating transport equation

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

2D heat equation
Example · Torch

2D heat equation

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

Transport equation in a cylinder
Example · Torch

Transport equation in a cylinder

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

Advection of a 3D level-set function
Example · Torch

Advection of a 3D level-set function

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

Learning the Signed Distance Function of a shape
Example · Torch

Learning the Signed Distance Function of a shape

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

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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