Physics-informed State-space Neural Networks for Transport Phenomena

TL;DR

This paper introduces Physics-informed State-space neural networks (PSMs) that incorporate physical laws into neural models for transport phenomena, improving accuracy, multitask capabilities, and potential for real-time system diagnostics and digital twins.

Contribution

The work presents a novel physics-informed neural network framework that integrates PDE constraints into state-space models for transport systems, enhancing accuracy and versatility over traditional data-driven methods.

Findings

PSMs outperform purely data-driven models with up to 94% error reduction.

Demonstrated multitask capabilities including control and diagnostics.

Potential application as digital twins for physical systems.

Abstract

This work introduces Physics-informed State-space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transport-dominated systems such as chemical, biomedical, and power plants. Traditional data-driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics-informing using components' Partial Differential Equations (PDEs), resulting in a physics-constrained, end-to-end differentiable forward dynamics model. Through two in silico experiments -- a heated channel and a cooling system loop -- we demonstrate that PSMs offer a more accurate approach than a purely data-driven model. In the former experiment, PSMs demonstrated significantly lower average root-mean-square errors across test datasets compared to a purely data-driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state-space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time-dependent constraints, while the latter illustrates their value in system diagnostics and fault detection. We further posit that PSMs could serve as a foundation for Digital Twins, constantly updated digital representations of physical systems.