Optimization of a partial differential equation on a complex network

07/17/2019
by   Martin Stoll, et al.
0

Differential equations on metric graphs can describe many phenomena in the physical world but also the spread of information on social media. To efficiently compute the solution is a hard task in numerical analysis. Solving a design problem, where the optimal setup for a desired state is given, is even more challenging. In this work, we focus on the task of solving an optimization problem subject to a differential equation on a metric graph with the control defined on a small set of Dirichlet nodes. We discuss the discretization by finite elements and provide rigorous error bounds as well as an efficient preconditioning strategy to deal with the large-scale case. We show in various examples that the method performs very robustly.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/20/2022

Finite difference and finite element methods for partial differential equations on fractals

In this paper, we present numerical procedures to compute solutions of p...
research
05/15/2022

A comparison of PINN approaches for drift-diffusion equations on metric graphs

In this paper we focus on comparing machine learning approaches for quan...
research
09/07/2018

Node classification for signed networks using diffuse interface methods

Signed networks are a crucial tool when modeling friend and foe relation...
research
09/20/2019

Computation and verification of contraction metrics for exponentially stable equilibria

The determination of exponentially stable equilibria and their basin of ...
research
05/03/2021

Optimal heating of an indoor swimming pool

This work presents the derivation of a model for the heating process of ...
research
07/17/2018

Confederated Modular Differential Equation APIs for Accelerated Algorithm Development and Benchmarking

Performant numerical solving of differential equations is required for l...
research
10/15/2022

Well-definedness of Physical Law Learning: The Uniqueness Problem

Physical law learning is the ambiguous attempt at automating the derivat...

Please sign up or login with your details

Forgot password? Click here to reset