LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processes

TL;DR

This paper introduces LQResNet, a deep neural network architecture that combines operator inference with deep learning to model complex dynamic processes, demonstrated on neural and biochemical systems.

Contribution

It proposes a novel integration of operator inference with deep neural networks for data-driven modeling of nonlinear dynamic systems.

Findings

Successfully modeled neural dynamics and glycolytic oscillators

Enhanced ability to infer unknown nonlinear dynamics

Combines prior knowledge with deep learning techniques

Abstract

Mathematical modeling is an essential step, for example, to analyze the transient behavior of a dynamical process and to perform engineering studies such as optimization and control. With the help of first-principles and expert knowledge, a dynamic model can be built, but for complex dynamic processes, appearing, e.g., in biology, chemical plants, neuroscience, financial markets, this often remains an onerous task. Hence, data-driven modeling of the dynamics process becomes an attractive choice and is supported by the rapid advancement in sensor and measurement technology. A data-driven approach, namely operator inference framework, models a dynamic process, where a particular structure of the nonlinear term is assumed. In this work, we suggest combining the operator inference with certain deep neural network approaches to infer the unknown nonlinear dynamics of the system. The approach uses recent advancements in deep learning and possible prior knowledge of the process if possible. We also briefly discuss several extensions and advantages of the proposed methodology. We demonstrate that the proposed methodology accomplishes the desired tasks for dynamics processes encountered in neural dynamics and the glycolytic oscillator.