Adaptive solution of initial value problems by a dynamical Galerkin scheme

11/08/2021
by   Rodrigo M. Pereira, et al.
0

We study dynamical Galerkin schemes for evolutionary partial differential equations (PDEs), where the projection operator changes over time. When selecting a subset of basis functions, the projection operator is non-differentiable in time and an integral formulation has to be used. We analyze the projected equations with respect to existence and uniqueness of the solution and prove that non-smooth projection operators introduce dissipation, a result which is crucial for adaptive discretizations of PDEs, e.g., adaptive wavelet methods. For the Burgers equation we illustrate numerically that thresholding the wavelet coefficients, and thus changing the projection space, will indeed introduce dissipation of energy. We discuss consequences for the so-called `pseudo-adaptive' simulations, where time evolution and dealiasing are done in Fourier space, whilst thresholding is carried out in wavelet space. Numerical examples are given for the inviscid Burgers equation in 1D and the incompressible Euler equations in 2D and 3D.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/28/2022

Pseudo-Differential Integral Operator for Learning Solution Operators of Partial Differential Equations

Learning mapping between two function spaces has attracted considerable ...
research
03/04/2023

Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations

Coupled partial differential equations (PDEs) are key tasks in modeling ...
research
12/16/2022

A certified wavelet-based physics-informed neural network for the solution of parameterized partial differential equations

Physics Informed Neural Networks (PINNs) have frequently been used for t...
research
11/24/2019

A Non-conditional Uniform Divergence Criteria of Projection Method for Compact Operator Equation

Projection methods (including collocation methods) are always considered...
research
11/21/2021

A Pseudo-Inverse for Nonlinear Operators

The Moore-Penrose inverse is widely used in physics, statistics and vari...
research
05/04/2022

Wavelet neural operator: a neural operator for parametric partial differential equations

With massive advancements in sensor technologies and Internet-of-things,...
research
12/06/2021

Projection methods for Neural Field equations

Neural field models are nonlinear integro-differential equations for the...

Please sign up or login with your details

Forgot password? Click here to reset