Relevant bibliographies by topics / PINN

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'PINN'

Author: Grafiati
Published: 30 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "PINN"

1

Abdullah, Ibrahima Faye, and Laila Amera Aziz. "Artificial Neural Networks Solutions for Solving Differential Equations: A Focus and Example for Flow of Viscoelastic Fluid with Microrotation." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 112, no. 1 (January 13, 2024): 76–83. http://dx.doi.org/10.37934/arfmts.112.1.7683.

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Physics-informed neural networks (PINN) are an artificial neural network (ANN) approach for solving differential equations. PINN offers an alternative to classical numerical methods. The paper discusses the applications of PINN in various domains by highlighting the advantages, challenges, limitations, and some future directions. For example, PINN is implemented to solve the differential equations describing the Flow of Viscoelastic Fluid with Microrotation at a Horizontal Circular Cylinder Boundary Layer. The differential equations resulting from a nondimensionalization process of the governing equations and the associated boundary conditions are solved using PINN. The obtained results using PINN are discussed and compared to other state-of-the-art methods. Future research might aim to increase the precision and effectiveness of PINN models for solving differential equations, either by adding more physics-based restrictions or multi-scale methods to expand their capabilities. Additionally, investigating new application domains like linked multi-physics issues or real-time simulation situations may help to increase the reach and significance of PINN approaches.
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Zhang, Wenjuan, and Mohammed Al Kobaisi. "On the Monotonicity and Positivity of Physics-Informed Neural Networks for Highly Anisotropic Diffusion Equations." Energies 15, no. 18 (September 18, 2022): 6823. http://dx.doi.org/10.3390/en15186823.

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Physics-informed neural network (PINN) models are developed in this work for solving highly anisotropic diffusion equations. Compared to traditional numerical discretization schemes such as the finite volume method and finite element method, PINN models are meshless and, therefore, have the advantage of imposing no constraint on the orientations of the diffusion tensors or the grid orthogonality conditions. To impose solution positivity, we tested PINN models with positivity-preserving activation functions for the last layer and found that the accuracy of the corresponding PINN solutions is quite poor compared to the vanilla PINN model. Therefore, to improve the monotonicity properties of PINN models, we propose a new loss function that incorporates additional terms which penalize negative solutions, in addition to the usual partial differential equation (PDE) residuals and boundary mismatch. Various numerical experiments show that the PINN models can accurately capture the tensorial effect of the diffusion tensor, and the PINN model utilizing the new loss function can reduce the degree of violations of monotonicity and improve the accuracy of solutions compared to the vanilla PINN model, while the computational expenses remain comparable. Moreover, we further developed PINN models that are composed of multiple neural networks to deal with discontinuous diffusion tensors. Pressure and flux continuity conditions on the discontinuity line are used to stitch the multiple networks into a single model by adding another loss term in the loss function. The resulting PINN models were shown to successfully solve the diffusion equation when the principal directions of the diffusion tensor change abruptly across the discontinuity line. The results demonstrate that the PINN models represent an attractive option for solving difficult anisotropic diffusion problems compared to traditional numerical discretization methods.
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Ang, Elijah Hao Wei, Guangjian Wang, and Bing Feng Ng. "Physics-Informed Neural Networks for Low Reynolds Number Flows over Cylinder." Energies 16, no. 12 (June 7, 2023): 4558. http://dx.doi.org/10.3390/en16124558.

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Physics-informed neural network (PINN) architectures are recent developments that can act as surrogate models for fluid dynamics in order to reduce computational costs. PINNs make use of deep neural networks, where the Navier-Stokes equation and freestream boundary conditions are used as losses of the neural network; hence, no simulation or experimental data in the training of the PINN is required. Here, the formulation of PINN for fluid dynamics is demonstrated and critical factors influencing the PINN design are discussed through a low Reynolds number flow over a cylinder. The PINN architecture showed the greatest improvement to the accuracy of results from the increase in the number of layers, followed by the increase in the number of points in the point cloud. Increasing the number of nodes per hidden layer brings about the smallest improvement in performance. In general, PINN is much more efficient than computational fluid dynamics (CFD) in terms of memory resource usage, with PINN requiring 5–10 times less memory. The tradeoff for this advantage is that it requires longer computational time, with PINN requiring approximately 3 times more than that of CFD. In essence, this paper demonstrates the direct formulation of PINN without the need for data, alongside hyperparameter design and comparison of computational requirements.
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Ekramoddoullah, Abul K. M., Doug Taylor, and Barbara J. Hawkins. "Characterisation of a fall protein of sugar pine and detection of its homologue associated with frost hardiness of western white pine needles." Canadian Journal of Forest Research 25, no. 7 (July 1, 1995): 1137–47. http://dx.doi.org/10.1139/x95-126.

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A 19-kDa protein of sugar pine (named Pinl I; i.e., protein I of Pinuslambertiana Dougl.) was detected in increasing amounts in the fall. This protein was separated by SDS–PAGE and also by two-dimensional gel electrophoresis. Pinl I was composed of two acidic isoforms. This protein was subjected to N-terminal amino acid sequence analysis. The two isoforms had an identical N-terminal amino acid sequence. The N-terminal peptide was synthesized and purified, and the purity of the synthetic peptide was greater than 95%. The peptide was conjugated to a carrier protein, keyhole limpet hemocyanin (KLH). Rabbits were immunized with peptide–KLH and the antipeptide antibody was purified from the crude antisera by immunoaffinity chromatography. The antibody was shown to bind specifically to PinI I. This anti-Pin I I antibody was used in a Western immunoblot to detect the homologues of Pin1 1 in two other five-needled pines: western white pine (Pinusmonticola Dougl.; named Pinm III) and eastern white pine (Pinusstrobus L.). The antibody was also used to monitor the seasonal variation of Pinm III in western white pine needles. Pinm III was shown to be associated with overwintering of western white pine seedlings. A significant correlation was observed between the frost hardiness of western white pine foliage and the content of Pinm III.
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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 properties. To address this issue, we propose a novel method that combines PICNN with the finite volume method to obtain physically plausible and conservative solutions to Navier-Stokes equations. We derive the second-order upwind difference scheme of Navier-Stokes equations using the finite volume method. Then we use the derived scheme to calculate the partial derivatives and construct the physics-informed loss function. The proposed method is assessed by experiments on steady-state Navier-Stokes equations under different scenarios, including convective heat transfer, lid-driven cavity flow, etc. The experimental results demonstrate that our method can effectively improve the plausibility and the accuracy of the predicted solutions from PICNN.
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Xiao, Zixu, Yaping Ju, Zhen Li, Jiawang Zhang, and Chuhua Zhang. "On the Hard Boundary Constraint Method for Fluid Flow Prediction based on the Physics-Informed Neural Network." Applied Sciences 14, no. 2 (January 19, 2024): 859. http://dx.doi.org/10.3390/app14020859.

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With the rapid development of artificial intelligence technology, the physics-informed neural network (PINN) has gradually emerged as an effective and potential method for solving N-S equations. The treatment of constraints is vital to the PINN prediction accuracy. Compared to soft constraints, hard constraints are advantageous for the avoidance of difficulties in guaranteeing definite conditions and determining penalty coefficients. However, the principles on the formulation of hard constraints of PINN currently remain to be formed, which hinders the application of PINN in engineering fields. In this study, hard-constraint-based PINN models are constructed for Couette flow, plate shear flow and stenotic/aneurysmal flow with curved geometries. Particular efforts have been devoted to assessing the impact of the model parameters of hard constraints, i.e., degree and scaling factor, on the prediction accuracy of PINN at different Reynolds numbers. The results show that the degree is the most important factor that influences the prediction accuracy, followed by the scaling factor. As for the N-S equations, the degree of hard constraints should be at least two, while the scaling factor is recommended to be maintained around 1.0. The outcomes of the present work are of reference value for the development of PINN methods in fluid mechanics.
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Xia, Yichun, and Yonggang Meng. "Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters." Lubricants 12, no. 2 (February 17, 2024): 62. http://dx.doi.org/10.3390/lubricants12020062.

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Ensuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment. Currently, the measurement of frictional contact temperature remains a formidable challenge, while the accuracy of simulation results from conventional numerical methods remains uncertain. In this study, a PINN model that incorporates physical information, such as partial differential equation (PDE) and boundary conditions, into neural networks is proposed to solve forward and inverse problems of frictional contact temperature. Compared to the traditional numerical calculation method, the preprocessing of the PINN is more convenient. Another noteworthy characteristic of the PINN is that it can combine data to obtain a more accurate temperature field and solve inverse problems to identify some unknown parameters. The experimental results substantiate that the PINN effectively resolves the forward problems of frictional contact temperature when provided with known input conditions. Additionally, the PINN demonstrates its ability to accurately predict the friction temperature field with an unknown input parameter, which is achieved by incorporating a limited quantity of easily measurable actual temperature data. The PINN can also be employed for the inverse identification of unknown parameters. Finally, the PINN exhibits potential in solving inverse problems associated with frictional contact temperature, even when multiple input parameters are unknown.
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Chen, Yanlai, Yajie Ji, Akil Narayan, and Zhenli Xu. "TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs." Computer Methods in Applied Mechanics and Engineering 430 (October 2024): 117198. http://dx.doi.org/10.1016/j.cma.2024.117198.

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Ngo, Son Ich, and Young-Il Lim. "Solution and Parameter Identification of a Fixed-Bed Reactor Model for Catalytic CO2 Methanation Using Physics-Informed Neural Networks." Catalysts 11, no. 11 (October 28, 2021): 1304. http://dx.doi.org/10.3390/catal11111304.

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In this study, we develop physics-informed neural networks (PINNs) to solve an isothermal fixed-bed (IFB) model for catalytic CO2 methanation. The PINN includes a feed-forward artificial neural network (FF-ANN) and physics-informed constraints, such as governing equations, boundary conditions, and reaction kinetics. The most effective PINN structure consists of 5–7 hidden layers, 256 neurons per layer, and a hyperbolic tangent (tanh) activation function. The forward PINN model solves the plug-flow reactor model of the IFB, whereas the inverse PINN model reveals an unknown effectiveness factor involved in the reaction kinetics. The forward PINN shows excellent extrapolation performance with an accuracy of 88.1% when concentrations outside the training domain are predicted using only one-sixth of the entire domain. The inverse PINN model identifies an unknown effectiveness factor with an error of 0.3%, even for a small number of observation datasets (e.g., 20 sets). These results suggest that forward and inverse PINNs can be used in the solution and system identification of fixed-bed models with chemical reaction kinetics.
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Hou, Qingzhi, Honghan Du, Zewei Sun, Jianping Wang, Xiaojing Wang, and Jianguo Wei. "PINN-CDR: A Neural Network-Based Simulation Tool for Convection-Diffusion-Reaction Systems." International Journal of Intelligent Systems 2023 (August 16, 2023): 1–15. http://dx.doi.org/10.1155/2023/2973249.

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In this paper, a discretization-free approach based on the physics-informed neural network (PINN) is proposed for solving the forward and inverse problems governed by the nonlinear convection-diffusion-reaction (CDR) systems. By embedding physical information described by the CDR system in the feedforward neural networks, PINN is trained to approximate the solution of the system without the need of labeled data. The good performance of PINN in solving the forward problem of the nonlinear CDR systems is verified by studying the problems of gas-solid adsorption and autocatalytic reacting flow. For CDR systems with different Péclet number, PINN can largely eliminate the numerical diffusion and unphysical oscillations in traditional numerical methods caused by high Péclet number. Meanwhile, the PINN framework is implemented to solve the inverse problem of nonlinear CDR systems and the results show that the unknown parameters can be effectively recognized even with high noisy data. It is concluded that the established PINN algorithm has good accuracy, convergence, and robustness for both the forward and inverse problems of CDR systems.
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Dissertations / Theses on the topic "PINN"

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Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.

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Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds. This project used a mathematical model describing a Continuous Stirred-Tank Reactor (CSTR), to test two possible applications of PINNs. The first type of PINN was trained to predict an unknown reaction rate law, based only on the differential equation and a time series of the reactor state. The resulting model was used inside a multi-step solver to simulate the system state over time. The results showed that the PINN could accurately model the behaviour of the missing physics also for new initial conditions. However, the model suffered from extrapolation error when tested on a larger reactor, with a much lower reaction rate. Comparisons between using a numerical derivative or automatic differentiation in the loss equation, indicated that the latter had a higher robustness to noise. Thus, it is likely the best choice for real applications. A second type of PINN was trained to forecast the system state one-step-ahead based on previous states and other known model parameters. An ordinary feed-forward neural network with an equal architecture was used as baseline. The second type of PINN did not outperform the baseline network. Further studies are needed to conclude if or when physics-informed loss should be used in autoregressive applications.
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Caple, Christopher. "An analytical appraisal of copper alloy pin production: 400-1600 AD : the development of the copper alloy, pin industry in Britain during the post-Roman period, based on analytical, metallographic and typological examination with consideration of historical and archaeological archives." Thesis, University of Bradford, 1986. http://hdl.handle.net/10454/3423.

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Pureswaran, Deepa S. "Dynamics of pheromone production and communication in the mountain pine beetle, Dendroctonus ponderosae Hopkins and the pine engraver, Ips pini (Say) (Coleoptera: Scolytidae)." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ51452.pdf.

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Grenon, Frank. "Relation entre la présence du nodulier (Petrova albicapitana) et les diminutions de la croissance du pin gris (Pinus banksiana) /." Thèse, Chicoutimi : Université du Québec à Chicoutimi, 1998. http://theses.uqac.ca.

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Young, Anna Gilg. "The isolation and characterization of geranyl diphosphate synthase from the pine engraver, Ips pini (Coleoptera: Scolytidae) /." abstract and full text PDF (UNR users only), 2004. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3164670.

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Hodnett, Kyle. "Mating and fitness consequences of breeding aggregations in pine engraver bark beetles, Ips pini (Coleoptera: scolytidae)." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65104.pdf.

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Pei, Ming Hao. "Peridermium pini (Pers.) Lév.-Axenic culture and infection of pine callus tissue cultures and young seedlings." Thesis, University of Aberdeen, 1989. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU553195.

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Axenic cultures of Peridermium pini have been established on modified Shenk &'38 Hilderbrandt's and Harvey &'38 Grasham's media from naturally infected cortex tissues and aeciospore-infected calluses of P. sylvestris and from aeciospores collected from NE Scotland and East Anglia. The cultures occasionally produced immature smooth-surfaced, binucleate spores. Actively growing cultures infected P. sylvestris calluses but not seedlings or trees. In the experiment of fungal nutrition, (NH4)2SO4 appeared essential, sucrose, D-glucose, raffinose and D-sorbitol supported good growth while D-xylose, cellobinose and L-arabinose did not. Opt. medium pH proved to be 5.0-6.0. Axenic cultures were also obtained from 30-40&'37 of single sporelings of some East Anglia spore sources but not from NE Scotland sporelings. When inoculated at a high density, however, all spore sources from both East Anglia and NE Scotland readily formed colonies. Colonies from East Anglia spores mostly appeared smooth at the surface and distinct around margin while those from NE Scotland sources had fluffy surface and irregularly extended periphery. Rapidly expanding hyphal layers developed from both of the colony forms 3 months after inoculation. Callus tissue cultures of P. sylvestris, P. nigra var. maritima and P. mugo vars mughus, rostrata and pumilio were infected by inoculation with aeciospores from NE Scotland. Infections were characterized by formation of aerial hyphae on the callus surface and intercellular hyphae and typical haustoria in the callus tissue. Hyphae from some of the infected calluses penetrated the medium. Seedlings of the pines as above were infected at their cotyledon stage by inoculation with NE Scotland spores. Infections resulted in swelling, death of the seedlings and formation of spermogonia after a year and aecia after two years. Infections of young seedlings of 7 seed sources of P. sylvestris and the UK were examined 6 weeks after inoculation. Discolouration and necrosis of cotyledons were not always related to stem infection.
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Robertson, Ian Charles. "Paternal care in the pine engraver, Ips pini (Coleoptera: Scolytidae), and the implications of variable reproductive potential for population dymamics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ37748.pdf.

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Evrard, Alexandre. "Etude des interactions cellulaires des puroindolines et étude de la régulation de l'expression des gènes PinA et PinB de blé." Montpellier, ENSA, 2003. http://www.theses.fr/2003ENSA0012.

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Les puroindolines sont des protéines de 13 kDa qui sont impliquées entre autre dans la friabilité du grain de blé tendre. La fonction cellulaire des puroindolines et la régulation de l'expression des gènes qui les codent sont peu des domaines de recherche peu explorés. Nous avons donc étudié les interactions cellulaires des puroindolines dans un système hétérologue, la levure Saccaromyces cerevisiae. Les puroindolines ne forment pas d'homodimères ni d'hétérodimères mais elles interagissent in vivo avec la membrane plasmique de la levure. Par mutagénèse dirigée nous avons montré que pour la puroindoline-a cette interaction implique le domaine riche en tryptophane, alors que ce n'est le cas pour la puroindoline-b. Parallèlement, les promoteurs des gènes PinA et PinB ont été étudiés dans des riz transgéniques. Alors que le gène PinB s'exprime uniquement dans le grain, le gène PinA s'exprime aussi dans d'autres organes et est inductible par blessure dans les tiges et les feuilles de riz
Puroindolines are 13kDa proteins, involved in wheat grain softness. Nethertheless, Cell function and Pin genes expression regulation are not very well documented. Puroindolines cell interactions were studied in the yeast Saccaromyces cerevisiae. Puroindolines do not form homo or heterodimer but interact in vivo with the yeast plasma membrane. Site directed mutagenesis approach highlighted that the tryptophan rich domain of puroindoline-a is involved in this interaction but not in the case of puroindoline-b. In parallel, promoter of both PinA and PinB genes were studied in transgenic rice plants. PinA and PinB genes are expressed in the grain and regulated during development. Whereas PinB gene expression is grain specific, PinA gene is expressed also in other organs is wound induced in stems and leaves
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Goodall, Benjamin. "Identification of novel factors contributing to the regulation of PIN-FORMED 7 (PIN7) transcription, in the Arabidopsis root." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/50036/.

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Understanding root development and patterning is important for both nutrient and water uptake. The Arabidopsis thaliana primary root has a di-arch vascular pattern consisting of a central xylem axis, perpendicular phloem poles and intervening procambial cells. Governance of this pattern involves a dynamic, antagonistic interaction between domains of auxin and cytokinin signalling bias. Here, one element of this auxin-cytokinin relationship; cytokinin’s indirect transcriptional regulation of the auxin PIN-FORMED 7 (PIN7) efflux transporter, has been investigated. Two complementary strategies were employed; transcriptomic profiling of an Type-B ARABIDOPSIS RESPONSE REGULATOR (ARR) response (the last known components in the core cytokinin signalling machinery) via an inducible glucocorticoid system, and an EMS mutagenesis based forward genetic screen of reduced PIN7::PIN7:GFP expression and subsequent genomic resequencing to identify potential causative agents. Both workflows produced novel candidate PIN7 regulators and the ensuing candidate validation revealed ETHYLENE RESPONSE FACTOR 104 (ERF104), CYTOKININ OXIDASE/DEHYDROGENASE 5 (CKX5) and the ECA1-like AT5G36520 from its vascular over-expressor DOUBLE PROTOXYLEM (DPX) phenotype, in particular as strong contenders for components involved in the regulation of PIN7 and patterning of the vascular cylinder.
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Books on the topic "PINN"

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Scott, De Buitléir, ed. Blaiseadh pinn: Nuascríbhneoireacht Ghaeilge. [Dublin, Ireland?]: Cois Life, 2008.

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1940-, Prút Liam, ed. Cuimhní pinn, cuimhní cinn. Binn Éadair, BÁC: Coisceím, 2010.

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Gréagóir, Ó Dúill, ed. Fearann Pinn: Filíochta 1900-1999. Baile Átha Cliath: Coiscéim, 2000.

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Gréagóir, Ó Dúill, ed. Fearann pinn: Filíocht, 1900-1999. Binn Éadair, Baile Átha Cliath: Coiscéim, 2000.

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Regina, Uí Chollatáin, ed. Iriseoirí pinn na Gaeilge: An cholúnaíocht liteartha : critic iriseoireachta. Baile Átha Cliath: Cois Life Teo., 2008.

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Shusen, Yao, ed. Bao ping qi an: Zhong pian ping shu. Shenyang: Chun feng wen yi chu ban she, 1985.

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Shusen, Yao, ed. Bao ping qi an: Zhong pian ping shu. Shenyang: Chun feng wen yi chu ban she, 1985.

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Zhou, Can. Zhou Can ping jie shi 30 pian. Singapore: Xinjiapo qing nian shu ju, 2012.

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Shusen, Yao, ed. Shemingwang chuan qi: Zhong pian ping shu. Shenyang: Chun feng wen yi chu ban she, 1985.

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Lewisohn, Ludwig. Jin dai wen yi pi ping duan pian. [Beijing: Beijing zhong xian tuo fang ke ji fa zhan you xian gong si, 2012.

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Book chapters on the topic "PINN"

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Ibrahim, Abdul Qadir, Sebastian Götschel, and Daniel Ruprecht. "Parareal with a Physics-Informed Neural Network as Coarse Propagator." In Euro-Par 2023: Parallel Processing, 649–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39698-4_44.

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AbstractParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal’s single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.
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Dhamirah Mohamad, Najwa Zawani, Akram Yousif, Nasiha Athira Binti Shaari, Hasreq Iskandar Mustafa, Samsul Ariffin Abdul Karim, Afza Shafie, and Muhammad Izzatullah. "Heat Transfer Modelling with Physics-Informed Neural Network (PINN)." In Studies in Systems, Decision and Control, 25–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_3.

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Li, Yan, Mingzhou Yang, Matthew Eagon, Majid Farhadloo, Yiqun Xie, William F. Northrop, and Shashi Shekhar. "Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 838–46. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch94.

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Saha, Subrata. "Physics Informed Neural Network (PINN) for Load Reconstruction for Vibrating Pipes in Process Plants." In International Conference on Security, Surveillance and Artificial Intelligence (ICSSAI-2023), 282–89. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003428459-32.

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Szerszeń, Krzysztof, and Eugeniusz Zieniuk. "Coupling PIES and PINN for Solving Two-Dimensional Boundary Value Problems via Domain Decomposition." In Computational Science – ICCS 2024, 87–94. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63759-9_11.

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Kalash, Taj Khan. "Jinn Pinn Dance in the Floods: Perceptions of Flood Disasters Among the Kalasha of Pakistan." In Dealing with Disasters, 101–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56104-8_5.

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Ye, Yubo, Huafeng Liu, Xiajun Jiang, Maryam Toloubidokhti, and Linwei Wang. "A Spatial-Temporally Adaptive PINN Framework for 3D Bi-Ventricular Electrophysiological Simulations and Parameter Inference." In Lecture Notes in Computer Science, 163–72. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43990-2_16.

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Yan, Da, and Ligang He. "DP-PINN: A Dual-Phase Training Scheme for Improving the Performance of Physics-Informed Neural Networks." In Computational Science – ICCS 2024, 19–32. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63749-0_2.

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van Laerhoven, Kristof, Albrecht Schmidt, and Hans-Werner Gellersen. "Pin&Play: Networking Objects through Pins." In UbiComp 2002: Ubiquitous Computing, 219–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45809-3_17.

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van Cranenburgh, Ben. "Inleiding: de studie van pijn." In Pijn, 19–33. Houten: Bohn Stafleu van Loghum, 2016. http://dx.doi.org/10.1007/978-90-368-1604-5_1.

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Conference papers on the topic "PINN"

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My Ha, Dao, Chiu Pao-Hsiung, Wong Jian Cheng, and Ooi Chin Chun. "Physics-Informed Neural Network With Numerical Differentiation for Modelling Complex Fluid Dynamic Problems." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-81237.

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Abstract In this study, novel physics-informed neural network (PINN) methods are proposed to allow efficient training with improved accuracy. The computation of differential operators required for loss evaluation at collocation points are conventionally obtained via automatic differentiation (AD). Such PINNs require large optimization iterations and are very sample intensive because they are prone to optimizing towards unphysical solutions without sufficient collocation points. To make PINN training sample efficient, the idea of using numerical differentiation, coupled with automatic differentiation, is employed to define the loss function. The proposed coupled-automatic-numerical differentiation scheme — labeled as can-PINN — strongly links the collocation points, thus enabling efficient training in sparse sample regimes. The superior performance of can-PINNs is demonstrated on several challenging PINN problems, including the rotational flow problem and the channel flow over a backward facing step problem. The results reveal that for the challenging problems, can-PINNs can always achieve very good accuracy while the conventional PINNs based on AD fail.
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Malineni, Vamsi Sai Krishna, and Suresh Rajendran. "On the Performance of a Data-Driven Backward Compatible Physics Informed Neural Network (BC-PINN) for Prediction of Flow Past a Cylinder." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-105343.

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Abstract This paper discusses a sparse data driven approach for training Physics Informed Neural Networks (PINNs) to solve the Navier-Stokes equation. A benchmark problem of 2D unsteady laminar flow past a cylinder is chosen for comparing the accuracy of existing PINN training approaches to the proposed methodology. The proposed training scheme is an improvement on the existing Backward Compatible PINN (BC-PINN) methodology. We have demonstrated that the performance of BC-PINN methodology in solving Navier Stokes equation is at a similar level to that of Standard PINN methodology using mini-batches. The proposed methodology resulted in a significant increase in accuracy in comparison with vanilla BC-PINN. Furthermore, transfer learning of parameters of a pre-trained model for different Reynolds numbers has resulted in a reduction of training time.
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Laubscher, Ryno, Pieter Rousseau, and Chris Meyer. "Modeling of Inviscid Flow Shock Formation in a Wedge-Shaped Domain Using a Physics-Informed Neural Network-Based Partial Differential Equation Solver." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-81768.

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Abstract Physics-informed neural networks (PINN) can potentially be applied to develop computationally efficient surrogate models, perform anomaly detection, and develop time-series forecasting models. However, predicting small-scale features such as the exact location of shocks and the associated rapid changes in fluid properties across it, have proven to be challenging when using standard PINN architectures, due to spatial biasing during network training. This paper investigates the ability of PINNs to capture these features of an oblique shock by applying Fourier feature network architectures. Four PINN architectures are applied namely a standard PINN architecture with the direct and indirect implementation of the ideal gas equation of state, as well as the direct implementation combined with a standard and modified Fourier feature transformation function. The case study is 2D steady-state compressible Euler flow over a 15° wedge at a Mach number of 5. The PINN predictions are compared to results generated using proven numerical CFD techniques. The results show that the indirect implementation of the equation of state is unable to enforce the prescribed boundary conditions. The application of the Fourier feature up-sampling to the low-dimensional spatial coordinates improves the ability of the PINN model to capture the small-scale features, with the standard implementation performing better than the modified version.
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Maniglio, Marco, Giorgio Fighera, Laura Dovera, and Carlo Cristiano Stabile. "Physics Informed Neural Networks Based on a Capacitance Resistance Model for Reservoirs Under Water Flooding Conditions." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207800-ms.

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Abstract In recent years great interest has risen towards surrogate reservoir models based on data-driven methodologies with the purpose of speeding up reservoir management decisions. In this work, a Physics Informed Neural Network (PINN) based on a Capacitance Resistance Model (CRM) has been developed and tested on a synthetic and on a real dataset to predict the production of oil reservoirs under waterflooding conditions. CRMs are simple models based on material balance that estimate the liquid production as a function of injected water and bottom hole pressure. PINNs are Artificial Neural Networks (ANNs) that incorporate prior physical knowledge of the system under study to regularize the network. A PINN based on a CRM is obtained by including the residual of the CRM differential equations in the loss function designed to train the neural network on the historical data. During training, weights and biases of the network and parameters of the physical equations, such as connectivity factors between wells, are updated with the backpropagation algorithm. To investigate the effectiveness of the novel methodology on waterflooded scenarios, two test cases are presented: a small synthetic one and a real mature reservoir. Results obtained with PINN are compared with respect to CRM and ANN alone. In the synthetic case CRM and PINN give slightly better quality history matches and predictions than ANN. The connectivity factors estimated by CRM and PINN are very similar and correctly represent the underlying geology. In the real case PINN gives better quality history matches and predictions than ANN, and both significantly outperform CRM. Even though the CRM formulation is too simple to predict the complex behavior of a real reservoir, the CRM based regularization contributes to improving the PINN predictions quality compared to the purely data-driven ANN model. The connectivity factors estimated by CRM and PINN are not in agreement. However, the latter method provided results closer to our understanding of the flooding process after many years of operations and data analysis. All considered, PINN outperformed both CRM and ANN in terms of predictivity and interpretability, effectively combining strengths from both methodologies. The presented approach does not require the construction of a 3D model since it learns directly from production data, while preserving physical consistency. Moreover, it represents a computationally inexpensive alternative to traditional full-physics reservoir simulations which could have vast applications for problems requiring many forward evaluations, like the optimization of water allocation for mature reservoirs.
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Ghaderi, Aref, and Roozbeh Dargazany. "Modeling the Burning of Polymer Matrix: Training Collocation Physics-Informed Neural Network." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95456.

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Abstract The combined impacts of thermal, chemical, and physical processes play a significant role in the pyrolysis problems in polymeric materials. Thermal energy is transported into the material via thermal convection when the charring materials are subjected to high-temperature loading. Decomposition of the resin will result in pyrolytic gases and solid leftovers. The material can be split into three zones based on the degree of pyrolysis of the material: (i) charring zone, in which the material entirely decomposes; (ii) pyrolysis zone, in which the material is disintegrating; and (iii) virgin material zone, in which the material has not yet begun to decompose. Physics-informed neural networks (PINNs) are neural networks whose components contain model equations, such as partial differential equations (PDEs). A multi-task learning approach has emerged in which a NN must fit observed data while decreasing a PDE residual. This article introduces PINN architectures to forecast temperature distributions and the degree of burning of a pyrolysis problem in a one-dimensional (1D) and two-dimensional (2D) rectangular domain. The complex, non-convex multi-objective loss function presents substantial obstacles for forward problems in training PINNs. We discovered that adding several differential relations to the loss function causes an unstable optimization issue, which may lead to convergence to the trivial null solution or significant deviation of the solution. To address this problem, the dimensionless form of the coupled governing equations that we find most beneficial to the optimizer is used. The numerical results are compared with results obtained from PINN to show the performance of the solution. Our research is the first to explore fully coupled temperature-degree-of-burning relationships in pyrolysis problems. Unlike classical numerical methods, the proposed PINN does not depend on domain discretization. In addition to these characteristics, the proposed PINN achieves good accuracy in predicting solution variables, which makes it a candidate to be utilized for surrogate modeling of pyrolysis problems. In summary, the pyrolysis model of materials is solved with the PINN framework; We assumed that all thermal properties of a material (thermal conductivity, specific heat, and density) are affected by temperature and degree of burning. While the achieved results are close to our expectations, it should be noted that training PINNs is time-consuming. We relate the training challenge to the multi-objective optimization issue and the application of a first-order optimization algorithm, as reported by others. Given the difficulties encountered and overcome in this work for the forward problem, the next step is to use PINNs to inverse burning situations.
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Chu, Haoyu, Yuto Miyatake, Wenjun Cui, Shikui Wei, and Daisuke Furihata. "Structure-Preserving Physics-Informed Neural Networks with Energy or Lyapunov Structure." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/428.

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Recently, there has been growing interest in using physics-informed neural networks (PINNs) to solve differential equations. However, the preservation of structure, such as energy and stability, in a suitable manner has yet to be established. This limitation could be a potential reason why the learning process for PINNs is not always efficient and the numerical results may suggest nonphysical behavior. Besides, there is little research on their applications on downstream tasks. To address these issues, we propose structure-preserving PINNs to improve their performance and broaden their applications for downstream tasks. Firstly, by leveraging prior knowledge about the physical system, a structure‐preserving loss function is designed to assist the PINN in learning the underlying structure. Secondly, a framework that utilizes structure-preserving PINN for robust image recognition is proposed. Here, preserving the Lyapunov structure of the underlying system ensures the stability of the system. Experimental results demonstrate that the proposed method improves the numerical accuracy of PINNs for partial differential equations (PDEs). Furthermore, the robustness of the model against adversarial perturbations in image data is enhanced.
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Alhubail, Ali, Marwan Fahs, Francois Lehmann, and Hussein Hoteit. "Physics-Informed Neural Networks for Modeling Flow in Heterogeneous Porous Media: A Decoupled Pressure-Velocity Approach." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24362-ms.

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Abstract Physics-informed neural networks (PINNs) have shown success in solving physical problems in various fields. However, PINNs face major limitations when addressing fluid flow in heterogeneous porous media, related to discontinuities in rock properties. This is because automatic differentiation is inadequate for evaluating the spatial derivatives of hydraulic conductivity where it is discontinuous. This study aims to devise PINN implementations that overcome this limitation. This work proposes decoupling the mass conservation equation from Darcy's law and utilizing the residuals of these decoupled equations to train the loss function of the PINN, rather than using a single residual from the combined equation. As a result, we circumvent the need to find the spatial derivative of the discontinuous hydraulic conductivity, and instead, we impose the continuity of fluxes. This decoupling necessitates that each primary unknown (pressure and velocity components) be computed by the neural networks (NNs) rather than deriving the velocity (or fluxes) from the pressure. We examined three NN configurations and compared their performance by analyzing their accuracy and training time for various 2D scenarios. These scenarios explored various boundary conditions, different hydraulic conductivity fields, as well as different orientations of the heterogeneous media within the domain of interest. In these problems, the pressure and velocity field are the primary unknowns. The three configurations include: (a) one NN with the three unknowns as its outputs, (b) two NNs, one outputting pressure and the other outputting the velocity, and (c) three NNs, each having one primary unknown as an output. Utilizing these NN architectures, we were able to solve the heterogeneous problems with varying levels of accuracy when compared to results from numerical simulators. While maintaining a similar number of training parameters for a fair assessment, the configuration with three NNs yielded the most accurate results, with a comparable training time to the other configurations. Using this optimal configuration, we performed a sensitivity analysis to demonstrate the effect of modifying the NN(s) hyperparameters, such as the number of layers, the number of nodes per layer, and the learning rate, on the accuracy of the results. We introduce a novel PINN approach for modeling fluid flow in heterogeneous media. This proposed method not only preserves the inherent discontinuity of rock petrophysical properties but also leverages the benefits of automatic differentiation. By incorporating this PINN architecture, we have opened up new possibilities for extending the application of PINN to realistic reservoir simulations that capture the complexities of the subsurface.
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Chen, Bo-Chen, and Yi-Hsiang Yu. "A Preliminary Study of Learning a Wave Energy Converter System Using Physics-Informed Neural Network Method." In ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/omae2023-105123.

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Abstract Physics-informed neural network (PINN) is a new type of neural network method that can be used to solve the physical problem by providing a given data set in the machine learning process with embedded physics information directly described by differential equations. A PINN model was applied in this study to solve the governing equation of motion for the analysis of a floating sphere wave energy converter (WEC), and a time-series segmentation approach was implemented to effectively handle the time-dependent problem. A series of PINN simulations are presented in this paper, including a decay test and a set of wave conditions, where the PINN solutions are verified against those obtained from other time-domain numerical models. This work aims to investigate the feasibility of using PINN for WEC applications with a focus on numerical benchmarking, and the studies of collection point resolution, the overall model accuracy, and the corresponding computational efficiency are also investigated.
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Dale, Seth, Doug Turner, Salar Afra, Adriana Teixeira, Leandro Saraiva Valim, Carolyn Koh, and Dinesh Mehta. "Physics-Informed Neural Networks for Gas Hydrate Plugging Risk Assessment Using Intrinsic Kinetics and Flowloop Data." In Offshore Technology Conference. OTC, 2024. http://dx.doi.org/10.4043/35362-ms.

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Abstract A reliable means of hydrate plugging risk assessment in pipelines is critical to the modern practice of production in the hydrate management regime. Flow assurance engineers utilize computationally expensive multiphase flow simulations to characterize hydrate formation at desired conditions, however, there is no numerical method to assess the risk of a plug occurring from these results. Traditional machine learning models have shown reasonably accurate plugging risk classification and require just milliseconds to return an assessment. Despite this, there has been limited industry use due to concerns about the statistical nature of predictions and the sparsity of available training data. Deep neural networks (DNNs) are a purely data-driven machine learning model that require large quantities of labeled data to make accurate statistical predictions in their trained domain. Physics-informed neural networks (PINNs) are a variation of DNNs in which training additionally considers embedded domain physics, in the form of partial differential equations, to increase accuracy, lessen reliance on training data, and ground predictions. This work presents a PINN that has been trained to predict hydrate plugging risk. Training was directed by the mean squared error of the model's prediction against flowloop data and, critically, the residual of the hydrate intrinsic kinetics equation. The trained model showed improved accuracy over reference DNNs. A PINN of novel architecture embedded with the hydrate intrinsic kinetics equation was built in TensorFlow. Flowloop data from pilot-scale flowloops was used for the training and evaluation of the presented PINN. Performance was compared to two DNNs for plugging risk assessment. DNN1 was an earlier model presented at OTC 2019. DNN2 features identical architecture to the subject PINN but absent of the embedded physics. DNN1 was employed as a baseline for plugging risk assessment performance, whereas DNN2 was used to isolate the contribution of the embedded domain knowledge on inference accuracy. The PINN showed a plugging risk assessment accuracy of 98.7%, which is a meaningful improvement over the 95.0% accuracy offered by DNN1. Moreover, case studies show improved confidence in plug prediction. The effect of the embedded physics on model accuracy is quantified by a reduction in mean squared error of 13.3% in inference of hydrate volume fraction when compared to DNN2. These findings indicate that the increased accuracy is the result of the embedding of the hydrate intrinsic kinetics equation as well as the novel network architecture. Two additional PINNs were presented, further establish the superior behavior of PINNs in learning the solution to PDEs and under data-sparse conditions. This work provides a new approach for machine learning in hydrates by demonstrating a technique to accurately train neural networks through a combination of empirical data and domain knowledge. This line of research could ultimately lead to more informed quantification of hydrate plugging risk.
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Fuchi, Kazuko W., Eric M. Wolf, David S. Makhija, Nathan A. Wukie, Christopher R. Schrock, and Philip S. Beran. "Investigation of Analysis and Gradient-Based Design Optimization Using Neural Networks." In ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/smasis2020-2241.

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Abstract Design optimization of adaptive systems requires a robust analysis method that can accommodate various changes in design and boundary conditions. In this work, physics-informed neural networks (PINNs) are used to approximate solutions to differential equations across a range of problem parameter values. This mesh-free method simply requires residual evaluation at sampling points within the analysis domain and along boundaries, and the training process does not require any reference problem to be solved through conventional solution methods. The trained model can be used to predict the solution field, conduct parameter space analysis and design optimization. Using automatic differentiation, the design objective and their derivatives can be computed as a post process for a gradient-based design optimization. The method is demonstrated in a 1D heat transfer problem governed by the steady-state heat equation. Use of the PINN model for design optimization is illustrated in a problem of finding a material transition location to minimize temperature at a specified location. The PINN model that does not include problem parameters as input can be trained to within 0.05% error. PINN models that involve problem parameters as inputs are more difficult to train, especially when the input-to-output relationship is complex.
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Reports on the topic "PINN"

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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.
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Motter, J. W., J. D. Pitcher, M. Fankhanel, and W. Campbell. Pinon Pine IGCC project status update, August 1992. Office of Scientific and Technical Information (OSTI), November 1992. http://dx.doi.org/10.2172/10106871.

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P. R. Fresquez, J. D. Huchton, M. A. Mullen, and Jr L. Naranjo. Pinon Pine Tree Study, Los Alamos National Laboratory: Source document. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/752387.

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Wulf, Gerburg M., and Kun P. Lu. Roles of Mitotic Checkpoint Regulators Pin1 and Pin2 in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada424006.

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Wulf, Gerburg M., and Kung P. Lu. Roles of the Mitotic Checkpoint Regulators Pin1 and Pin2 in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada404683.

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Ward, Kimiora. Sierra Nevada Network white pine monitoring: 2022 annual report. National Park Service, 2023. http://dx.doi.org/10.36967/2301003.

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Five-needle white pines (Family Pinaceae, Genus Pinus, Subgenus Strobus), and in particular whitebark pine (Pinus albicaulis), limber pine (P. flexilis), and foxtail pine (P. balfouriana) are foundation species in upper subalpine and treeline forests of several National Park Service Pacific West Region parks, including Sequoia and Kings Canyon National Parks (SEKI) and Yosemite National Park (YOSE). The Sierra Nevada Network Inventory & Monitoring Program, in collaboration with the Klamath Network, Upper Columbia Basin Network, and Mojave Desert Network have implemented a joint long-term monitoring protocol to assess the current status and future trends in high elevation white pine communities. Key demographic parameters within white pine forest communities will be estimated by monitoring individual trees within permanent plots through time. This report documents the results of the 2022 field season, which was the ninth year of monitoring in SEKI and YOSE. The 2021 goal was to complete the first full measure of the third of three rotating panels (Panel 3) for each species-park population: YOSE-whitebark pine, SEKI-whitebark pine, and SEKI-foxtail pine. Each panel consists of 12 permanent 50 x 50 m (2,500 m2) plots that were randomly selected for each of the three populations. The full sampling array thus includes a total of 36 whitebark pine plots in YOSE, 36 whitebark pine plots in SEKI, and 36 foxtail pine plots in SEKI. Data from plot surveys will be used to characterize white pine forest community dynamics in SEKI and YOSE, including changes in tree species composition, forest structure, forest health, and demographics. Partial measures of Panel 3 were completed in 2017 (11 plots) in Yosemite whitebark pine, in 2017 (9 plots) in SEKI whitebark pine, and in 2014 (7 plots) and 2017-2018 (8, 1 plots) in foxtail pine. In 2022, the first full measure of all Panel 3 plots (and 2nd or 3rd remeasure of most plots) was successfully completed, and installation was completed on four of these plots in SEKI whitebark pine and two in foxtail pine. In total, the crew visited 36 sites during the 2022 field season, all from Panel 3. Within the 36 completed Panel 1 plots, a total of 6,398 trees were measured. Species composition, forest structure, and factors affecting tree health and reproduction including incidence and severity of white pine blister rust (Cronartium ribicola) infection, mountain pine beetle (Dendroctonus ponderosae) infestation, dwarf mistletoe (Arceuthobium spp.) infection, canopy kill, and female cone production were recorded. During the 2022 field season crews continued to count the total number of mature cones per tree for whitebark and foxtail pine, use crown condition codes to assess crown health, and tag individual seedlings to be tracked through time. All three of these procedures started in 2017 and are to be evaluated by each of the three participating networks over several years, to determine whether they should become permanent changes to the monitoring protocol. In YOSE, all 12 Panel 3 whitebark pine plots were measured. A total of 2,720 trees were sampled, which included 977 live whitebark pine trees and 1,605 other live conifers. An additional 135 trees (including 26 whitebark) were recorded as dead. The average number of live whitebark pine trees per plot was 81 (SD = 94). White pine blister rust (WPBR) aecia were observed on five whitebark pine in one plot in YOSE in 2022, and no trees in any plot had inactive cankers showing three or more indicators of WPBR. WPBR had previously been documented in this plot, so the number of plots where rust has ever been observed in Yosemite remains unchanged at six. However, an infection documented in plot 42 in 2021 was not observed again when the plot was resampled in 2022, so it is possible this number should be five. Mountain pine beetle activity was observed on one live whitebark pine and three live and one dead lodgepole pine in YOSE in 2022. Despite documentation of many stands impacted by beetle attack in the field crew notes, the quantified rate of MPB attack was lower than in 2021. Twenty-one percent of live whitebark pine trees produced female cones. Cone-bearing trees averaged 7 (SD = 10) cones/tree. Whitebark pine seedling density averaged 80 (SD = 152) seedlings per hectare. The largest number of whitebark pine seedlings found in a plot was 51 and five of the twelve plots contained whitebark seedlings. All 12 Panel 3 SEKI whitebark pine plots were measured in 2022, and installation was completed on four of these, so this Panel is now fully installed. Within these plots, 2,179 live whitebark pine, 10 live foxtail pine, and 297 other live conifers were sampled (including 5 live western white pine). The average number of live whitebark pine trees per plot was 181 (SD = 125). Although the crew observed white pine blister rust in seven SEKI whitebark Panel 3 plots, no active cankers (aecia) were observed, and no trees displayed 3 of 5 indicators, so no infections were quantified. Mountain pine beetle activity was observed in 18 live and 23 dead whitebark pine and 1 live and one dead lodgepole pine within three plots in SEKI. Dwarf mistletoe was not encountered. Seven percent of live whitebark pine trees produced female cones. Cone-bearing trees averaged 3.7 (SD = 3.6) cones/tree. Whitebark seedling regeneration averaged 700 (SD = 752) seedlings per hectare. The largest number of whitebark seedlings found in a plot was 19, and two of the 12 plots did not contain any whitebark seedlings. In the foxtail pine Panel 3, all 12 plots were measured in 2022, and installation was completed on two of these, so installation of the panel is now complete. Within these plots we measured 309 live foxtail pine, 302 live whitebark pine, and 380 other live conifers, including four live western white pine. An additional 112 dead or recently dead trees and 22 unidentified snags were also measured, 19 of which were foxtail pine. The average number of foxtail pine trees per plot was 26 (SD = 26). No signs of blister rust infection or mistletoe were observed on foxtail pine. Mountain pine beetle activity was observed on one dead foxtail pine, one live whitebark pine, and seven live and one dead lodgepole pines within four plots. Sixty-two percent of the foxtail pine trees produced female cones. Cone-bearing trees averaged 33 (SD = 53) cones/tree. Seven foxtail pine seedlings were recorded within five plots, resulting in an estimated 72 (SD = 98) seedlings per hectare. Eight whitebark pine seedlings and three lodgepole pine seedlings were also found within three additional plots.
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Ward, Kimiora. Sierra Nevada Network high elevation white pine monitoring: 2021 annual report. National Park Service, 2024. http://dx.doi.org/10.36967/2302327.

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Five-needle white pines (Family Pinaceae, Genus Pinus, Subgenus Strobus), and in particular whitebark pine (Pinus albicaulis), limber pine (P. flexilis), and foxtail pine (P. balfouriana) are foundation species in upper subalpine and treeline forests of several National Park Service Pacific West Region parks, including Sequoia and Kings Canyon National Parks (SEKI) and Yosemite National Park (YOSE). The Sierra Nevada Network Inventory & Monitoring Program, in collaboration with the Klamath Network, Upper Columbia Basin Network, and Mojave Desert Network have implemented a joint long-term monitoring protocol to assess the current status and future trends in high elevation white pine communities. Key demographic parameters within white pine forest communities will be estimated by monitoring individual trees within permanent plots through time. This report documents the results of the 2021 field season, which was the eighth year of monitoring in Sequoia and Kings Canyon National Parks (SEKI) and Yosemite National Park (YOSE). The 2021 goal was to complete the third full re-measure of the second of three rotating panels (Panel 2) for each species-park population: YOSE-whitebark pine, SEKI-whitebark pine, and SEKI-foxtail pine. Each panel consists of 12 permanent 50 x 50 m (2,500 m2) plots that were randomly selected for each of the three populations. The full sampling array thus includes a total of 36 whitebark pine plots in YOSE, 36 whitebark pine plots in SEKI, and 36 foxtail pine plots in SEKI. Data from plot surveys will be used to characterize white pine forest community dynamics in SEKI and YOSE, including changes in tree species composition, forest structure, forest health, and demographics. The first full measure of all Panel 2 plots was completed over two years in 2013-2014, then a full remeasure of both parks? whitebark pine Panel 2 was conducted in 2016, with 10 of 12 SEKI-foxtail plots sampled that year. A third remeasure of all Panel 2 plots was not possible in 2021 because a smaller crew size was necessary during the COVID-19 pandemic. In total, the crew visited 37 sites, and sampled 31, during the 2021 field season. One plot in the YOSE whitebark pine frame was uninstalled before reading and one plot in the SEKI whitebark pine frame was uninstalled after reading, both for safety concerns. Four plots were not visited due to lack of capacity with the reduced crew size: one in each of the YOSE and SEKI whitebark frames, and three in the SEKI foxtail frame. A plot from Panel 3 in each of the parks? whitebark frames was measured, for a total of 11 plots measured in each whitebark pine frame. Nine plots were measured in the SEKI foxtail pine frame. Within the 31 plots completed, a total of 5,728 trees was measured. Species composition, forest structure, and factors affecting tree health and reproduction, including incidence and severity of white pine blister rust (Cronartium ribicola) infection, mountain pine beetle (Dendroctonus ponderosae) infestation, dwarf mistletoe (Arceuthobium spp.) infection, canopy kill, female cone production and regeneration were recorded. During the 2021 field season, crews continued to count the total number of mature cones per tree for whitebark and foxtail pine, use crown condition codes to assess crown health, and tag individual seedlings to be tracked through time. All three of these procedures started in 2017 and are to be evaluated by each of the three participating networks over several years, to determine whether they should become permanent changes to the monitoring protocol. In YOSE, 11 whitebark pine plots were re-measured, from Panels 2 and 3. A total of 2,810 trees were sampled, which included 586 live whitebark pine trees and 2,097 other live conifers. An additional 127 trees (including 17 whitebark pine) were recorded as dead. The forest crew noted little sign of white pine blister rust (WPBR) in Yosemite in 2021, and just a single inactive canker was observed on one whitebark pine in Panel 3, Plot 42, near Dana Meadows. This infection was new to plot 42, and it expands the total number of plots where white pine blister rust has been documented in Yosemite to six. The crew also noted little mountain pine beetle activity, documenting beetle galleries on 15 lodgepole pines in three Panel 2 plots. Dwarf mistletoe was not observed. The average number of live whitebark pine trees per plot was 53 (SD = 56). This was a low cone crop year for whitebark pine, with two percent of live whitebark pine trees producing female cones. Cone bearing trees averaged 2 (SD = 1) cones per tree. Whitebark pine seedling density averaged 90 (SD = 157) seedlings per hectare. The largest number of whitebark pine seedlings found in a plot was four, and three of the eleven plots contained whitebark seedlings. In SEKI, 10 of 12 Panel 2, and one Panel 3, whitebark pine plots were re-measured. Within these plots, 1,246 live whitebark pine, 30 live foxtail pine, and 861 other live conifers were sampled. WPBR was infrequently documented in the SEKI whitebark frame as well, with indicators of infection in Plot 31 near Window Creek and Plot 44 near Upper State Lake. These were the first infections documented in these plots, bringing the number of plots where WPBR has been documented in the SEKI whitebark panel to nine. Although WPBR was documented in Plot 27 near Charlotte Dome in 2016, it was not documented this year because putative cankers showing three signs of infection in 2016 showed only two or fewer signs in 2021. Mountain pine beetle activity was observed in one live lodgepole pine and two recently dead whitebark pine, within three plots in the SEKI whitebark sample frame. An exception to the low levels of mountain pine beetle activity was outside Plot 31 in the Window Creek area, where the forest crew noted many recently dead whitebark pine with signs of beetle activity. Dwarf mistletoe was not encountered. The average number of live whitebark pine trees per plot was 113 (SD = 86). Less than one percent of live whitebark pine trees produced female cones, each producing on average 2 (SD = 1) cones. Whitebark seedling regeneration averaged 303 (SD = 319) seedlings per hectare. The largest number of whitebark seedlings found in a plot was eight, and eight of the 11 plots contained whitebark seedlings. Nine of the 12 SEKI foxtail Panel 3 plots were remeasured. Within these plots, 413 live foxtail pine, 67 live whitebark pine, and 402 other live conifers were sampled. Ninety-two dead or recently dead trees were also documented, 65 of which were foxtail pine. No signs of blister rust infection, mistletoe, or mountain pine beetle were observed in the foxtail plots sampled. The average number of foxtail pine trees per plot was 46 (SD = 33). Fifty-four percent of the foxtail pine trees produced female cones, averaging 14 (SD =15) cones/tree. Only one foxtail pine seedling was recorded within the 9 foxtail pine plots, resulting in an estimated 14 (SD = 41) seedlings per hectare. Eight whitebark pine seedlings were also found within two plots.
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8

Onuoha, Chukwuma Candidus, and Shamus McDonnell. PR-388-143604-R01 Identifying Coating Faults and Severity Through Electrolyte Resistivity Measurement. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2018. http://dx.doi.org/10.55274/r0011481.

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Abstract:
Soil resistivity is one of the most important variables that is used to ascertain the corrosivity of an electrolyte around a pipe. This is crucial information that enhances the classification, prioritization and selection of locations prone to external corrosion during External Corrosion Direct Assessment (ECDA) or pipeline integrity assessment when combined with results from cathodic protection and coating surveys. The Wenner Four-Pin method is commonly used in the industry for soil resistivity measurements. It entails perpendicular measurements with four equidistant pins away from the pipe to avoid any interference from the buried metallic pipe. However, the resistivity of soil samples taken away from the pipe might not be similar to the actual soil condition at the pipe location, possibly due to environmental polarization and changes in soil mixing due to previous construction activities. This could lead to an erroneous prioritization of coating faults and soil corrosivity assessment during pipeline integrity corrosion evaluations. In this study, measurements of soil resistivity conducted at different depths on top of and adjacent to a buried coated pipe using the Wenner Four-Pin method indicated that this method is less susceptible to errors arising from interference from the buried coated metallic pipe. The resistance of the coating actually increased the measured resistivity. This confirms that soil resistivity measurements should be taken on top of the pipe whenever possible to improve the prioritization of coating faults and ensure pipeline integrity.
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9

Blanchard, J. P., and N. M. Ghoniem. Bowing of solid breeder fuel pins and multiplier rods in a pin-type fusion blanket. Office of Scientific and Technical Information (OSTI), February 1986. http://dx.doi.org/10.2172/5550859.

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10

Poloboc, Alina. Fancy Pink Goat. Intellectual Archive, December 2023. http://dx.doi.org/10.32370/iaj.2998.

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"Fancy Pink Goat" is a contemporary art piece from the Fancy Collection, created in Spain in 2022. It is a vividly colorful painting dominated by pink and blue, which are the signature colors of the artist`s style. The painting features a fancy goat walking through the jungle with its elegant collar and abstract, long legs. Surrounding the Fancy Pink Goat are a variety of other unusual creatures inhabiting the jungle and keeping the goat company. The artist`s signature red high-heeled shoes are also present, adding a touch of sophistication and style to the painting. This artwork is an impressive example of the artist`s unique style, which blends elements of surrealism and abstraction to create a sense of fantasy and wonder. The overall effect is an intriguing and vibrant work of art that captures the viewer`s imagination. With its expert technique and distinctive style, "Fancy Pink Goat" is truly a gem in the Fancy Collection.
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