基于灵敏度的自适应采样鲁棒PINN建模:最优传感器放置和结构不确定性处理的集成

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Shuji Chang , Piyush Agarwal , Chris McCready , Luis Ricardez-Sandoval , Hector Budman
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引用次数: 0

摘要

物理信息神经网络(pinn)已成为由偏微分方程(PDEs)控制的系统的有前途的替代模型,但其实际实施面临着训练效率和对不确定性的鲁棒性方面的挑战。本研究改进了最近提出的基于灵敏度的自适应采样(SBS)方法,以解决这些挑战,特别是对于二次可微PDE系统。我们首先对SBS超参数进行了系统的研究,包括预测视界和自适应率,揭示了它们在训练绩效中的关键作用。为了增强PINN模型面对不确定性的鲁棒性,我们提出了两种方法:(1)将传感器在灵敏度识别位置的测量结果纳入损失函数,以及(2)用直接传感器数据输入增强PINN架构。结果表明,我们提出的方法具有较好的泛化能力和鲁棒性。此外,我们证明了SBS方法可以通过识别最大化模型训练信息增益的位置来实现最佳传感器放置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust PINN modeling via sensitivity-based adaptive sampling: Integration of optimal sensor placement and structural uncertainty handling
Physics-informed neural networks (PINNs) have emerged as promising surrogate models for systems governed by partial differential equations (PDEs), yet their practical implementation faces challenges in training efficiency and robustness to uncertainties. This study refines and improves a recently proposed sensitivity-based adaptive sampling (SBS) methodology to address these challenges specifically for twice-differentiable PDE systems. We first conduct a systematic investigation of SBS hyper-parameters, including prediction horizon and adaptation rate, revealing their crucial role in training performance. To enhance PINN model robustness facing uncertainties, we propose two approaches: (1) incorporating sensor measurements at sensitivity-identified locations into the loss function, and (2) augmenting the PINN architecture with direct sensor data inputs. Results show that our proposed approaches achieve superior generalization capabilities and robustness. Furthermore, we demonstrate that the SBS methodology can serve for optimal sensor placement by identifying locations that maximize information gain for model training.
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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