Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations

• URL: http://arxiv.org/abs/2506.06188v1
• Date: Fri, 06 Jun 2025 15:50:19 GMT
• Title: Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
• Authors: Luis Kin Miyatake, Eduardo Camponogara, Eric Aislan Antonelo, Alexey Pavlov,
• Abstract summary: We extend the Physics-Informed Neural Nets for Control (PINC) framework to integrate neural networks with physical conservation laws.<n>The PINC model for PDEs is structured into two stages: a steady-state network, which learns equilibrium solutions for a wide range of control inputs, and a transient network, which captures dynamic responses under time-varying boundary conditions.<n>We validate our approach through numerical experiments, demonstrating that the PINC model, which is trained exclusively using physical laws, accurately represents flow dynamics and enables real-time control applications.
• Score: 4.776073133338117
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The modeling and control of single-phase flow systems governed by Partial Differential Equations (PDEs) present challenges, especially under transient conditions. In this work, we extend the Physics-Informed Neural Nets for Control (PINC) framework, originally proposed to modeling and control of Ordinary Differential Equations (ODE) without the need of any labeled data, to the PDE case, particularly to single-phase incompressible and compressible flows, integrating neural networks with physical conservation laws. The PINC model for PDEs is structured into two stages: a steady-state network, which learns equilibrium solutions for a wide range of control inputs, and a transient network, which captures dynamic responses under time-varying boundary conditions. We propose a simplifying assumption that reduces the dimensionality of the spatial coordinate regarding the initial condition, allowing the efficient training of the PINC network. This simplification enables the derivation of optimal control policies using Model Predictive Control (MPC). We validate our approach through numerical experiments, demonstrating that the PINC model, which is trained exclusively using physical laws, i.e., without labeled data, accurately represents flow dynamics and enables real-time control applications. The results highlight the PINC's capability to efficiently approximate PDE solutions without requiring iterative solvers, making it a promising alternative for fluid flow monitoring and optimization in engineering applications.

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