Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis

Lawal, Zaharaddeen Karami; Yassin, Hayati; Lai, Daphne Teck Ching; Idris, Azam Che

doi:10.3390/bdcc6040140

Cited by 31 publications

(14 citation statements)

References 114 publications

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“…• Recently, many researchers attempted to extend the PINN algorithms to overcome these challenges [172].…”

Section: Discussionmentioning

confidence: 99%

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Rizvi,

Abbas

2023

Eng. Res. Express

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Owing to recent advancements in sensor technology, data mining, machine learning and cloud computation, structural health monitoring (SHM) based on a data-driven approach has gained more popularity and interest. The data-driven methodology has proved to be more efficient and robust compared with traditional physics-based methods. The past decade has witnessed remarkable progress in machine learning, especially in the field of deep learning which are effective in many tasks and has achieved state-of-the-art results in various engineering domains. In the same manner, deep learning has also revolutionized SHM technology by improving the effectiveness and efficiency of models, as well as enhancing safety and reliability. To some extent, it has also paved the way for implementing SHM in real-world complex civil and mechanical infrastructures. However, despite all the success, deep learning has intrinsic limitations such as its massive-labelled data requirement, inability to generate consistent results and lack of generalizability to out-of-sample scenarios. Conversely, in SHM, the lack of data corresponding to a different state of the structure is still a challenging task. Recent development in physics-informed machine learning methods has provided an opportunity to resolve these challenges in which limited-noisy data and mathematical models are integrated through machine learning algorithms. This method automatically satisfies physical invariants providing better accuracy and improved generalization. This manuscript presents the sate of- -the-art review of prevailing machine learning methods for efficient damage inspection, discuss their limitations, and explains the diverse applications and benefits of physics-informed machine learning in the SHM setting. Moreover, the latest data extraction strategy and the internet of things (IoT) that support the present data-driven methods and SHM are also briefly discussed in the last section.

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“…• Recently, many researchers attempted to extend the PINN algorithms to overcome these challenges [172].…”

Section: Discussionmentioning

confidence: 99%

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Rizvi,

Abbas

2023

Eng. Res. Express

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“…The use of PINNs is currently being studied as a potential replacement for existing numerical techniques. Due to the recent advent of this type of neural networks, the literature is not yet massive, but reports particularly important pivotal works such as e.g [3,4,5,6,7,8,9,10,11,12,13,14,15,16]. A comprehensive review can be also found in [17].…”

Section: Introductionmentioning

confidence: 99%

Learning systems of ordinary differential equations with Physics-Informed Neural Networks: the case study of enzyme kinetics

Lecca

2024

J. Phys.: Conf. Ser.

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Physics Informed Neural Networks (PINNs) are a type of function approximators that use both data-driven supervised neural networks to learn the model of the dynamics of a physical system, and mathematical equations of the physical laws governing that system. PINNs have the benefit of being data-driven to train a model, but also of being able to assure consistency with the physics, and to extrapolate accurately beyond the range of data that currently accessible. As a result, PINNs can provide models that are more reliable while using less data. Specifically, the PINNs objective is to learn the solutions of a systems of equations using supervised learning on the available data and incorporating the knowledge of physical laws and constraints into the training process. However, solving single differential equations with a PINN may be relatively simple, solving systems of coupled differential equations may not be so simple. In this study, I present a neural network model specialized in solving differential equations of enzyme kinetics that has the main characteristic of being a demonstrative simple case of coupled equations system. The study focuses mainly on the theoretical aspects of the definition of a physics-informed loss function and shows a case study that highlights the challenges still to be overcome in solving systems of coupled differential equations.

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“…PINNs are particularly useful in fields where conventional data-driven approaches may fall short due to limited or noisy data. By incorporating biological laws and constraints within the PINN framework, they can effectively solve a range of differential equations, including partial differential equations (PDEs), integral-differential equations, and stochastic PDEs [14], making them highly suitable for studying brain dynamics models (BDMs).…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks

Sotero,

Sanchez-Bornot,

Shaharabi-Farahani

2024

Preprint

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Conventional modeling of the Blood-Oxygen-Level-Dependent (BOLD) signal in resting-state functional Magnetic Resonance Imaging (rsfMRI) struggle with parameter estimation due to the complexity of brain dynamics. This study introduces a novel brain dynamics model (BDM) that directly captures BOLD signal variations through differential equations. Unlike dynamic causal models or neural mass models, we integrate hemodynamic responses into the signal dynamics, considering both direct and network-mediated neuronal activity effects. We utilize Physics-Informed Neural Networks (PINNs) to estimate the parameters of this BDM, leveraging their ability to embed physical laws into the learning process. This approach simplifies computational demands and increases robustness against data noise, providing a comprehensive tool for analyzing rsfMRI data. Leveraging the functional connectivity matrices scaled by the estimated parameters, we apply a state-of-the-art community detection method to elucidate the network structure. Our analysis reveals significant differences in the participation coefficients of specific brain regions when comparing neurotypical individuals to those with Autism Spectrum Disorder (ASD), with distinct patterns observed between male and female cohorts. These differences are consistent with regions implicated in previous studies, reinforcing the role of these areas in ASD. By integrating PINNs with advanced network analysis, we demonstrate a robust approach for dissecting the complex neural signatures of ASD, providing a promising direction for future research in neuroimaging and the broader field of computational neuroscience.

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Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis

Cited by 31 publications

References 114 publications

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Learning systems of ordinary differential equations with Physics-Informed Neural Networks: the case study of enzyme kinetics

Parameter Estimation in Brain Dynamics Models from Resting-State fMRI Data using Physics-Informed Neural Networks

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