Analog simulator of integro-differential equations with classical memristors

TL;DR

This paper introduces a novel analog computer design using memristors to simulate a broad class of linear and nonlinear integro-differential equations, demonstrating robustness and potential for complex system modeling.

Contribution

It is the first to propose memristor-based circuits for simulating integro-differential equations, expanding the capabilities of analog computers beyond traditional linear differential equations.

Findings

Successfully simulated fluid dynamics and population growth models.

Achieved robust solutions with up to 13% relative error.

Demonstrated stability despite imperfect components.

Abstract

An analog computer makes use of continuously changeable quantities of a system, such as its electrical, mechanical, or hydraulic properties, to solve a given problem. While these devices are usually computationally more powerful than their digital counterparts, they suffer from analog noise which does not allow for error control. We will focus on analog computers based on active electrical networks comprised of resistors, capacitors, and operational amplifiers which are capable of simulating any linear ordinary differential equation. However, the class of nonlinear dynamics they can solve is limited. In this work, by adding memristors to the electrical network, we show that the analog computer can simulate a large variety of linear and nonlinear integro-differential equations by carefully choosing the conductance and the dynamics of the memristor state variable. To the best of our knowledge, this is the first time that circuits based on memristors are proposed for simulations. We study the performance of these analog computers by simulating integro-differential models related to fluid dynamics, nonlinear Volterra equations for population growth, and quantum models describing non-Markovian memory effects, among others. Finally, we perform stability tests by considering imperfect analog components, obtaining robust solutions with up to $13\%$ relative error for relevant timescales.