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Artículos de revistas sobre el tema "Physics-Informed neural network"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Trahan, Corey, Mark Loveland, and Samuel Dent. "Quantum Physics-Informed Neural Networks." Entropy 26, no. 8 (2024): 649. http://dx.doi.org/10.3390/e26080649.

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In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed, and hybrid configurations are explored. The results show that (1) for some applications, quantum PINNs can obtain comparable accuracy with less neural network parameters than classical PINNs, and (2) adding quantum nodes in classical PINNs can in

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2

Wang, Jing, Yubo Li, Anping Wu, et al. "Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations." Applied Sciences 14, no. 13 (2024): 5490. http://dx.doi.org/10.3390/app14135490.

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This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints. Different from traditional neural networks for solving partial differential equations, the proposed method uses a deep neural operator network to indirectly construct the mapping relationship between the variable functions and solution functions. This approach makes full use of the hidden information between the variable functions and independent variables. The process whereby the model cap

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3

Hofmann, Tobias, Jacob Hamar, Marcel Rogge, Christoph Zoerr, Simon Erhard, and Jan Philipp Schmidt. "Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries." Journal of The Electrochemical Society 170, no. 9 (2023): 090524. http://dx.doi.org/10.1149/1945-7111/acf0ef.

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One of the most challenging tasks of modern battery management systems is the accurate state of health estimation. While physico-chemical models are accurate, they have high computational cost. Neural networks lack physical interpretability but are efficient. Physics-informed neural networks tackle the aforementioned shortcomings by combining the efficiency of neural networks with the accuracy of physico-chemical models. A physics-informed neural network is developed and evaluated against three different datasets: A pseudo-two-dimensional Newman model generates data at various state of health

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4

Pu, Ruilong, and Xinlong Feng. "Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation." Entropy 24, no. 8 (2022): 1106. http://dx.doi.org/10.3390/e24081106.

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In this paper, a grid-free deep learning method based on a physics-informed neural network is proposed for solving coupled Stokes–Darcy equations with Bever–Joseph–Saffman interface conditions. This method has the advantage of avoiding grid generation and can greatly reduce the amount of computation when solving complex problems. Although original physical neural network algorithms have been used to solve many differential equations, we find that the direct use of physical neural networks to solve coupled Stokes–Darcy equations does not provide accurate solutions in some cases, such as rigid t

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5

Li, Zhenyu. "A Review of Physics-Informed Neural Networks." Applied and Computational Engineering 133, no. 1 (2025): 165–73. https://doi.org/10.54254/2755-2721/2025.20636.

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This article presents Physics-Informed Neural Networks (PINNs), which integrate physical laws into neural network training to model complex systems governed by partial differential equations (PDEs). PINNs enhance data efficiency, allowing for accurate predictions with less training data, and have applications in fields such as biomedical engineering, geophysics, and material science. Despite their advantages, PINNs face challenges like learning high-frequency components and computational overhead. Proposed solutions include causality constraints and improved boundary condition handling. A nume

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6

Kenzhebek, Y., T. S. Imankulov, and D. Zh Akhmed-Zaki. "PREDICTION OF OIL PRODUCTION USING PHYSICS-INFORMED NEURAL NETWORKS." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (2021): 45–50. http://dx.doi.org/10.51889/2021-4.1728-7901.06.

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In recent years, modern information technologies have been actively used in various industries. The oil industry is no exception, since high-performance computing technologies, artificial intelligence algorithms, methods of collecting, processing and storing information are actively used to solve the problems of increasing oil recovery. Deep learning has made remarkable strides in a variety of applications, but its use for solving partial differential equations has only recently emerged. In particular, you can replace traditional numerical methods with a neural network that approximates the so

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7

Hou, Shubo, Wenchao Wu, and Xiuhong Hao. "Physics-informed neural network for simulating magnetic field of permanent magnet." Journal of Physics: Conference Series 2853, no. 1 (2024): 012018. http://dx.doi.org/10.1088/1742-6596/2853/1/012018.

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Abstract With the rapid development of deep learning, its application in physical field simulation has been widely concerned, and it has begun to lead a new model of meshless simulation. In this paper, research based on physics-informed neural networks is carried out to solve partial differential equations related to the physical laws of electromagnetism. Then the magnetic field simulation is realized. In this method, the governing equation and the boundary conditions containing physical information are embedded into the neural network loss function as constraints, and the backpropagation of n

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8

Pan, Cunliang, Shi Feng, Shengyang Tao, Hongwu Zhang, Yonggang Zheng, and Hongfei Ye. "Physics-Informed Neural Network for Young-Laplace Equation." International Conference on Computational & Experimental Engineering and Sciences 30, no. 1 (2024): 1. http://dx.doi.org/10.32604/icces.2024.011132.

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9

Yoon, Seunghyun, Yongsung Park, and Woojae Seong. "Improving mode extraction with physics-informed neural network." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A339—A340. http://dx.doi.org/10.1121/10.0023729.

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This study aims to enhance conventional mode extraction methods in ocean waveguides using a physics-informed neural network (PINN). Mode extraction involves estimating mode wavenumbers and corresponding mode depth functions. The approach considers a scenario with a single frequency source towed at a constant depth and measured from a vertical line array (VLA). Conventional mode extraction methods applied to experimental data face two problems. First, mode shape estimation is limited because the receivers only cover a partial waveguide. Second, the wavenumber spectrum is affected by issues such

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10

Satyadharma, Adhika, Heng-Chuan Kan, Ming-Jyh Chern, and Chun-Ying Yu. "Numerical error estimation with physics informed neural network." Computers & Fluids 299 (August 2025): 106700. https://doi.org/10.1016/j.compfluid.2025.106700.

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11

Schmid, Johannes D., Philipp Bauerschmidt, Caglar Gurbuz, and Steffen Marburg. "Physics-informed neural networks for characterization of structural dynamic boundary conditions." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A99. http://dx.doi.org/10.1121/10.0022923.

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Structural dynamics simulations are often faced with challenges arising from unknown boundary conditions, leading to considerable prediction uncertainties. Direct measurement of these boundary conditions can be impractical for certain mounting scenarios, such as joints or screw connections. In addition, conventional inverse methods face limitations in integrating measured data and solving inverse problems when the forward model is computationally expensive. In this study, we explore the potential of physics-informed neural networks that incorporate the residual of a partial differential equati

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12

Stenkin, Dmitry, and Vladimir Gorbachenko. "Mathematical Modeling on a Physics-Informed Radial Basis Function Network." Mathematics 12, no. 2 (2024): 241. http://dx.doi.org/10.3390/math12020241.

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The article is devoted to approximate methods for solving differential equations. An approach based on neural networks with radial basis functions is presented. Neural network training algorithms adapted to radial basis function networks are proposed, in particular adaptations of the Nesterov and Levenberg-Marquardt algorithms. The effectiveness of the proposed algorithms is demonstrated for solving model problems of function approximation, differential equations, direct and inverse boundary value problems, and modeling processes in piecewise homogeneous media.

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13

Zhai, Hanfeng, Quan Zhou, and Guohui Hu. "Predicting micro-bubble dynamics with semi-physics-informed deep learning." AIP Advances 12, no. 3 (2022): 035153. http://dx.doi.org/10.1063/5.0079602.

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Utilizing physical information to improve the performance of the conventional neural networks is becoming a promising research direction in scientific computing recently. For multiphase flows, it would require significant computational resources for neural network training due to the large gradients near the interface between the two fluids. Based on the idea of the physics-informed neural networks (PINNs), a modified deep learning framework BubbleNet is proposed to overcome this difficulty in the present study. The deep neural network (DNN) with separate sub-nets is adopted to predict physics

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14

Vo, Van Truong, Samad Noeiaghdam, Denis Sidorov, Aliona Dreglea, and Liguo Wang. "Solving Nonlinear Energy Supply and Demand System Using Physics-Informed Neural Networks." Computation 13, no. 1 (2025): 13. https://doi.org/10.3390/computation13010013.

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Nonlinear differential equations and systems play a crucial role in modeling systems where time-dependent factors exhibit nonlinear characteristics. Due to their nonlinear nature, solving such systems often presents significant difficulties and challenges. In this study, we propose a method utilizing Physics-Informed Neural Networks (PINNs) to solve the nonlinear energy supply–demand (ESD) system. We design a neural network with four outputs, where each output approximates a function that corresponds to one of the unknown functions in the nonlinear system of differential equations describing t

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15

Karakonstantis, Xenofon, and Efren Fernandez-Grande. "Advancing sound field analysis with physics-informed neural networks." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A98. http://dx.doi.org/10.1121/10.0022920.

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This work introduces a method that employs physics-informed neural networks to reconstruct sound fields in diverse rooms, including both typical acoustically damped meeting rooms and more spaces of cultural significance, such as concert halls or theatres. The neural network is trained using a limited set of room impulse responses, integrating the expressive capacity of neural networks with the fundamental physics of sound propagation governed by the wave equation. Consequently, the network accurately represents sound fields within an aperture without requiring extensive measurements, regardles

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16

Socaciu, Tiberiu, and Paul Pașcu. "Physics-Informed Neural Networks in Pricing Financial Options." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 16, no. 2 (2025): 474. https://doi.org/10.70594/brain/16.2/33.

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<p dir="ltr"><span>PINNs (Physics-Informed Neural Networks) are neural networks designed to solve Partial Differential Equations (PDEs) by integrating physical knowledge into the learning framework. Constructing a PINN involves defining a neural network to approximate the PDE solution, with the total loss calculated as a combination of the losses associated with the PDE, boundary conditions, initial conditions, and measured data. This concept is employed in practical applications to solve various PDEs, such as the Black-Scholes and Heston equations, which are fundamental in financi

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17

Dong, Chenghao. "Solving Differential Equations with Physics-Informed Neural Networks." Theoretical and Natural Science 87, no. 1 (2025): 137–46. https://doi.org/10.54254/2753-8818/2025.20346.

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Solving differential equations is an extensive topic in various fields, such as fluid mechanics and mathematical finance. The recent resurgence in deep neural networks has opened up a brand new track for numerically solving these equations, with the potential to better deal with nonlinear problems and overcome the curse of dimensionality. The Physics-Informed Neural Network (PINN) is one of the fundamental attempts to solve differential equations using deep learning techniques. This paper aims to briefly review the application of PINNs and their variants in solving differential equations throu

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18

Zhang, Jianlin, Yake Leng, Hui Dong, Jianuo Yang, and Yinglin Jiao. "Peakons of the shallow-water wave equation implementation with PINNs." Journal of Physics: Conference Series 2964, no. 1 (2025): 012051. https://doi.org/10.1088/1742-6596/2964/1/012051.

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Abstract In this paper, we implement the peakon solution of the CH equation containing highly nonlinear terms using the Physics-Informed Neural Networks, and we define the corresponding neural network models according to different forms of peakon solution expressions. The main implementation is to study the spatio-temporal dynamics behaviour of the peakon solution for the general form of the CH equations. From the experimental results, the Physics-Informed Neural Networks can basically complete the analytical solution prediction of the shallow-water wave equation, and achieve the accurate capt

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19

Pannekoucke, Olivier, and Ronan Fablet. "PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations." Geoscientific Model Development 13, no. 7 (2020): 3373–82. http://dx.doi.org/10.5194/gmd-13-3373-2020.

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Abstract. Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue. In the spirit of physics-informed neural networks (NNs), the PDE-NetGen package provides new means to automatically translate physical equations, given as partial differential equations (PDEs), into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The latter exploits NN-based implementations of PDE solvers using Keras. With s

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20

Karakonstantis, Xenofon, Diego Caviedes-Nozal, Antoine Richard, and Efren Fernandez-Grande. "Room impulse response reconstruction with physics-informed deep learning." Journal of the Acoustical Society of America 155, no. 2 (2024): 1048–59. http://dx.doi.org/10.1121/10.0024750.

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A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks. By incorporating a limited set of experimental room impulse responses as training data, this approach combines neural network processing capabilities with the underlying physics of sound propagation, as articulated by the wave equation. The network's ability to estimate particle velocity and intensity, in addition to sound pressure, demonstrates its capacity to represent the flow of acoustic energy and completely characterise the sound field with only a few measurements

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21

Schmid, Johannes. "Physics-informed neural networks for solving the Helmholtz equation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 267, no. 1 (2023): 265–68. http://dx.doi.org/10.3397/no_2023_0049.

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Discretization-based methods like the finite element method have proven to be effective for solving the Helmholtz equation in computational acoustics. However, it is very challenging to incorporate measured data into the model or infer model input parameters based on observed response data. Machine learning approaches have shown promising potential in data-driven modeling. In practical applications, purely supervised approaches suffer from poor generalization and physical interpretability. Physics-informed neural networks (PINNs) incorporate prior knowledge of the underlying partial differenti

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22

Yoon, Seunghyun, Yongsung Park, Peter Gerstoft, and Woojae Seong. "Physics-informed neural network for predicting unmeasured ocean acoustic pressure field." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A97. http://dx.doi.org/10.1121/10.0022916.

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This study employs a physics-informed neural network in an ocean waveguide to predict the unmeasured acoustic pressure field, leveraging partially measured data. The method addresses a scenario where an acoustic source transmits signals across different ranges and is measured by multiple receivers. The acoustic pressure field in ocean waveguides exhibits rapid spatial variations over kilometer-range scales. The fully connected neural networks encounter challenges when approximating high-frequency functions, known as spectral bias. To mitigate this problem, the measured pressure field is transf

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23

Berrone, Stefano, and Moreno Pintore. "Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy." Algorithms 17, no. 9 (2024): 415. http://dx.doi.org/10.3390/a17090415.

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In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the triangulation of the entire domain and that can be trained with an adaptive set of test functions. In order to generate the test space, we exploit an a posteriori error indicator and add test functions only where the error is higher. Four training strategies are proposed and compared. Numerical results show that the accuracy is higher than the one of a Variational-Physics-Informed Neural Network trained with the same

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24

Bizzi, Arthur, Lucas Nissenbaum, and João M. Pereira. "Neural Conjugate Flows: A Physics-Informed Architecture with Flow Structure." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15576–86. https://doi.org/10.1609/aaai.v39i15.33710.

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We introduce Neural Conjugate Flows (NCF), a class of neural-network architectures equipped with exact flow structure. By leveraging topological conjugation, we prove that these networks are not only naturally isomorphic to a continuous group, but are also universal approximators for flows of ordinary differential equation (ODEs). Furthermore, topological properties of these flows can be enforced by the architecture in an interpretable manner. We demonstrate in numerical experiments how this topological group structure leads to concrete computational gains over other physics informed neural ne

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25

Ponomarev, R. Yu, R. R. Ziazev, A. A. Leshchenko, R. R. Migmanov, and M. I. Ivlev. "Flooding system optimization: Advantages of a hybrid approach to developing neural network filtration models." Actual Problems of Oil and Gas 15, no. 4 (2024): 349–63. https://doi.org/10.29222/ipng.2078-5712.2024-15-4.art3.

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Background. Currently, neural networks are increasingly used for processing and forecasting the dynamics of well performance. However, there are a number of limitations in their application for flooding system optimization. Objective. To develop models that can correctly reproduce the impact of the reservoir pressure maintenance system on the operation of production wells. The article considers the problem of modeling the reaction of producing wells to the changes in water injection modes in injection wells using neural network modeling methods. Results. We propose the approaches to the creati

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26

Yoon, Seunghyun, Yongsung Park, Peter Gerstoft, and Woojae Seong. "Predicting ocean pressure field with a physics-informed neural network." Journal of the Acoustical Society of America 155, no. 3 (2024): 2037–49. http://dx.doi.org/10.1121/10.0025235.

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Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases

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27

Hassanaly, Malik, Peter J. Weddle, Corey R. Randall, Eric J. Dufek, and Kandler Smith. "Rapid Inverse Parameter Inference Using Physics-Informed Neural Networks." ECS Meeting Abstracts MA2024-01, no. 2 (2024): 345. http://dx.doi.org/10.1149/ma2024-012345mtgabs.

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As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration ana

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28

Tarkhov, Dmitriy, Tatiana Lazovskaya, and Galina Malykhina. "Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor." Sensors 23, no. 2 (2023): 663. http://dx.doi.org/10.3390/s23020663.

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A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters of the governing equations as trainable parameters. Constructing the network is carried out in three stages. In the first step, a neural network solution to an equation corresponding to a numerical scheme is constructed. It allows for forming an initial low-fidelity neural network solution to the original problem. At the second stage, the network wi

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29

Cai, Zemin, Xiangqi Lin, Tianshu Liu, Fan Wu, Shizhao Wang, and Yun Liu. "Determining pressure from velocity via physics-informed neural network." European Journal of Mechanics - B/Fluids 109 (January 2025): 1–21. http://dx.doi.org/10.1016/j.euromechflu.2024.08.007.

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Liu, Xue, Wei Cheng, Ji Xing, et al. "Physics-informed Neural Network for system identification of rotors." IFAC-PapersOnLine 58, no. 15 (2024): 307–12. http://dx.doi.org/10.1016/j.ifacol.2024.08.546.

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31

Shi, Yan, and Michael Beer. "Physics-informed neural network classification framework for reliability analysis." Expert Systems with Applications 258 (December 2024): 125207. http://dx.doi.org/10.1016/j.eswa.2024.125207.

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Costa, Erbet Almeida, Carine Menezes Rebello, Vinícius Viena Santana, and Idelfonso B. R. Nogueira. "Physics-informed neural network uncertainty assessment through Bayesian inference." IFAC-PapersOnLine 58, no. 14 (2024): 652–57. http://dx.doi.org/10.1016/j.ifacol.2024.08.411.

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33

Hassanaly, Malik, Peter J. Weddle, Kandler Smith, Subhayan De, Alireza Doostan, and Ryan King. "Physics-Informed Neural Network Modeling of Li-Ion Batteries." ECS Meeting Abstracts MA2022-02, no. 3 (2022): 174. http://dx.doi.org/10.1149/ma2022-023174mtgabs.

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Li-ion batteries (LIB) are a promising solution to enable the storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (e.g., external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. In particular, the LIB degradation model needs to be transferable to variable operating conditions throughout

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34

Guler Bayazit, Nilgun. "Physics informed neural network consisting of two decoupled stages." Engineering Science and Technology, an International Journal 46 (October 2023): 101489. http://dx.doi.org/10.1016/j.jestch.2023.101489.

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Sha, Yanliang, Jun Lan, Yida Li, and Quan Chen. "A Physics-Informed Recurrent Neural Network for RRAM Modeling." Electronics 12, no. 13 (2023): 2906. http://dx.doi.org/10.3390/electronics12132906.

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Extracting behavioral models of RRAM devices is challenging due to their unique “memory” behaviors and rapid developments, for which well-established modeling frameworks and systematic parameter extraction processes are not available. In this work, we propose a physics-informed recurrent neural network (PiRNN) methodology to generate behavioral models of RRAM devices from practical measurement/simulation data. The proposed framework can faithfully capture the evolution of internal state and its impacts on the output. A series of modifications informed by the RRAM device physics are proposed to

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36

Liu, Chen-Xu, Xinghao Wang, Weiming Liu, Yi-Fan Yang, Gui-Lan Yu, and Zhanli Liu. "A physics-informed neural network for Kresling origami structures." International Journal of Mechanical Sciences 269 (May 2024): 109080. http://dx.doi.org/10.1016/j.ijmecsci.2024.109080.

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Silva, Roberto Mamud Guedes da, Helio dos Santos Migon, and Antônio José da Silva Neto. "Parameter estimation in the pollutant dispersion problem with Physics-Informed Neural Networks." Ciência e Natura 45, esp. 3 (2023): e74615. http://dx.doi.org/10.5902/2179460x74615.

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In this work, the inverse problem of parameter estimation in the advection-dispersion-reaction equation, modelling the pollutant dispersion in a river, is studied with a Neural Network approach. In the direct problem, the dispersion, velocity and reaction parameters are known and then the initial and boundary value problem is solved by classical numerical methods, where it is used as input dataset for the inverse problem and formulation. In the inverse problem, we know the dispersion and the velocity parameters and also the information about the pollutant concentration from the synthetic exper

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38

Liu, Zhixiang, Yuanji Chen, Ge Song, Wei Song, and Jingxiang Xu. "Combination of Physics-Informed Neural Networks and Single-Relaxation-Time Lattice Boltzmann Method for Solving Inverse Problems in Fluid Mechanics." Mathematics 11, no. 19 (2023): 4147. http://dx.doi.org/10.3390/math11194147.

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Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and thus ensure that their predictions are consistent and stable with the physical laws. PINNs open up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar–Gross–Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The

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39

Oluwasakin, Ebenezer O., and Abdul Q. M. Khaliq. "Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters." Algorithms 16, no. 12 (2023): 547. http://dx.doi.org/10.3390/a16120547.

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Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and more accurate ways to simulate dynamic systems. This research explores the transformative capabilities of physics-informed neural networks, a specialized subset of artificial neural networks, in modeling complex dynamical systems with enhanced speed and accuracy. These networks incorporate known physical laws into the learning process, ensuring

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40

Li, Jianfeng, Liangying Zhou, Jingwei Sun, and Guangzhong Sun. "Physically plausible and conservative solutions to Navier-Stokes equations using Physics-Informed CNNs." JUSTC 53 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0174.

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Physics-informed Neural Network (PINN) is an emerging approach for efficiently solving partial differential equations (PDEs) using neural networks. Physics-informed Convolutional Neural Network (PICNN), a variant of PINN enhanced by convolutional neural networks (CNNs), has achieved better results on a series of PDEs since the parameter-sharing property of CNNs is effective to learn spatial dependencies. However, applying existing PICNN-based methods to solve Navier-Stokes equations can generate oscillating predictions, which are inconsistent with the laws of physics and the conservation prope

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Hooshyar, Saman, and Arash Elahi. "Sequencing Initial Conditions in Physics-Informed Neural Networks." Journal of Chemistry and Environment 3, no. 1 (2024): 98–108. http://dx.doi.org/10.56946/jce.v3i1.345.

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The scientific machine learning (SciML) field has introduced a new class of models called physics-informed neural networks (PINNs). These models incorporate domain-specific knowledge as soft constraints on a loss function and use machine learning techniques to train the model. Although PINN models have shown promising results for simple problems, they are prone to failure when moderate level of complexities are added to the problems. We demonstrate that the existing baseline models, in particular PINN and evolutionary sampling (Evo), are unable to capture the solution to differential equations

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42

Kaliuzhniak, Anastasiia, Oleksii Kudi, Yuriy Belokon, and Dmytro Kruglyak. "Developing of neural network computing methods for solving inverse elasticity problems." Eastern-European Journal of Enterprise Technologies 6, no. 7 (132) (2024): 45–52. https://doi.org/10.15587/1729-4061.2024.313795.

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This paper examines the use of neural network methods to solve inverse problems in the mechanics of elastic materials. The aim is to design physics-informed neural networks that can predict the parameters of structural components, and the physical properties of materials based on a specified displacement distribution. A key feature of the specified neural networks is the integration of differential equations and boundary conditions into the loss function calculation. This approach ensures that the error in approximating unknown functions has a direct impact on optimizing the network's weights.

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43

Olivieri, Marco, Mirco Pezzoli, Fabio Antonacci, and Augusto Sarti. "A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography." Sensors 21, no. 23 (2021): 7834. http://dx.doi.org/10.3390/s21237834.

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In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surface of the vibrating structure using the sampled pressure soundfield on the holographic plane as input. The loss function used for training the network is based on a combination of two components. The first component is the error in the reconstructed velocity. The second component is the error between the sound pressure on the holographic plane and

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44

Yonekura, Kazuo. "A Short Note on Physics-Guided GAN to Learn Physical Models without Gradients." Algorithms 17, no. 7 (2024): 279. http://dx.doi.org/10.3390/a17070279.

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This study briefly describes the concept of guided training of deep neural networks (DNNs) to learn physically reasonable solutions. The proposed method does not need the gradients of the physical equations, although the conventional physics-informed models need the gradients. DNNs are widely used to predict phenomena in physics and mechanics. One of the issues with DNNs is that their output does not always satisfy physical equations. One approach to consider with physical equations is adding a residual of the equations into the loss function; this is called physics-informed neural network (PI

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45

Duarte, D. H. G., P. D. S. de Lima, and J. M. de Araújo. "Outlier-resistant physics-informed neural network." Physical Review E 111, no. 2 (2025). https://doi.org/10.1103/physreve.111.l023302.

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46

Manikkan, Sreehari, and Balaji Srinivasan. "Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing." Engineering with Computers, July 19, 2022. http://dx.doi.org/10.1007/s00366-022-01703-9.

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47

Liu Jin-Pin, Wang Bing-Zhong, Chen Chuan-Sheng, and Wang Ren. "Inverse design of microwave waveguide devices based on deep physics-informed neural networks." Acta Physica Sinica, 2023, 0. http://dx.doi.org/10.7498/aps.72.20230031.

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Using physics-informed neural networks to solve physical inverse problems is becoming a trend.However,it is difficult to solve the scheme that only introduces physical knowledge through the loss function.Constructing a reasonable loss function to make the results converge becomes a challenge.To address the challenge of physics-informed neural network models for inverse design of electromagnetic devices,a deep physics-informed neural network is introduced by using pattern matching method.The physical equations have been integrated into the network structure when the network is constructed.This

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48

Dourado, Arinan, and Felipe A. C. Viana. "Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis." Annual Conference of the PHM Society 11, no. 1 (2019). http://dx.doi.org/10.36001/phmconf.2019.v11i1.814.

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In this paper, we present a novel physics-informed neural network modeling approach for corrosion-fatigue. The hybrid approach is designed to merge physics- informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion). A numerical experiment is used to present the main features of the proposed physics-informed recurrent ne

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49

Zapf, Bastian, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide, and Kent-Andre Mardal. "Investigating molecular transport in the human brain from MRI with physics-informed neural networks." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-19157-w.

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AbstractIn recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by training a neural network. We apply physics-informed neural networks and the finite element method to estimate the diffusion coefficient governing the long term spread of molecules in the human brain from magnetic resonance images. Synthetic testcases are created to demonstrate that the standard formulation of the physics-informed neural netwo

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50

Wang, Shihai, Jiangyong He, Congcong Liu, et al. "Physics-Informed Neural Network for Fiber Amplifiers." Journal of Lightwave Technology, 2025, 1–6. https://doi.org/10.1109/jlt.2025.3545238.

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