Numerical Stability for Differential Equations with Memory

05/11/2023
by   Guihong Wang, et al.
0

In this work, we systematically investigate linear multi-step methods for differential equations with memory. In particular, we focus on the numerical stability for multi-step methods. According to this investigation, we give some sufficient conditions for the stability and convergence of some common multi-step methods, and accordingly, a notion of A-stability for differential equations with memory. Finally, we carry out the computational performance of our theory through numerical examples.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/11/2021

Novel multi-step predictor-corrector schemes for backward stochastic differential equations

Novel multi-step predictor-corrector numerical schemes have been derived...
research
03/09/2022

Small Errors Imply Large Instabilities

Numerical Analysts and scientists working in applications often observe ...
research
06/11/2020

Deep Differential System Stability – Learning advanced computations from examples

Can advanced mathematical computations be learned from examples? Using t...
research
06/01/2020

Stabilized explicit multirate methods for stiff differential equations

Stabilized Runge-Kutta (aka Chebyshev) methods are especially efficient ...
research
06/16/2021

Numerical Stability of Tangents and Adjoints of Implicit Functions

We investigate errors in tangents and adjoints of implicit functions res...
research
04/29/2019

Recurrent Neural Networks in the Eye of Differential Equations

To understand the fundamental trade-offs between training stability, tem...
research
04/27/2023

Hyperparameter optimization of orthogonal functions in the numerical solution of differential equations

This paper considers the hyperparameter optimization problem of mathemat...

Please sign up or login with your details

Forgot password? Click here to reset