Relevant bibliographies by topics / Physics Informed Neural Networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Physics Informed Neural Networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 December 2024

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Journal articles on the topic "Physics Informed Neural Networks"

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Trahan, Corey, Mark Loveland, and Samuel Dent. "Quantum Physics-Informed Neural Networks." Entropy 26, no. 8 (July 30, 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 increase model accuracy with less total network parameters for noiseless models.
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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 (September 1, 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 points. This dataset is fused with experimental data from laboratory measurements and vehicle field data to train a neural network in which it exploits correlation from internal modeled states to the measurable state of health. The resulting physics-informed neural network performs best with the synthetic dataset and achieves a root mean squared error below 2% at estimating the state of health. The root mean squared error stays within 3% for laboratory test data, with the lowest error observed for constant current discharge samples. The physics-informed neural network outperforms several other purely data-driven methods and proves its advantage. The inclusion of physico-chemical information from simulation increases accuracy and further enables broader application ranges.
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Pang, Guofei, Lu Lu, and George Em Karniadakis. "fPINNs: Fractional Physics-Informed Neural Networks." SIAM Journal on Scientific Computing 41, no. 4 (January 2019): A2603—A2626. http://dx.doi.org/10.1137/18m1229845.

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Song, Yanjie, He Wang, He Yang, Maria Luisa Taccari, and Xiaohui Chen. "Loss-attentional physics-informed neural networks." Journal of Computational Physics 501 (March 2024): 112781. http://dx.doi.org/10.1016/j.jcp.2024.112781.

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Rojas, Sergio, Paweł Maczuga, Judit Muñoz-Matute, David Pardo, and Maciej Paszyński. "Robust Variational Physics-Informed Neural Networks." Computer Methods in Applied Mechanics and Engineering 425 (May 2024): 116904. http://dx.doi.org/10.1016/j.cma.2024.116904.

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Henkes, Alexander, Henning Wessels, and Rolf Mahnken. "Physics informed neural networks for continuum micromechanics." Computer Methods in Applied Mechanics and Engineering 393 (April 2022): 114790. http://dx.doi.org/10.1016/j.cma.2022.114790.

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Chen, Haotian, Enno Kätelhön, and Richard G. Compton. "Predicting Voltammetry Using Physics-Informed Neural Networks." Journal of Physical Chemistry Letters 13, no. 2 (January 10, 2022): 536–43. http://dx.doi.org/10.1021/acs.jpclett.1c04054.

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Lee, Sang-Min. "Physics-Informed Neural Networks and its Applications." Journal of the Korea Academia-Industrial cooperation Society 23, no. 12 (December 31, 2022): 755–60. http://dx.doi.org/10.5762/kais.2022.23.12.755.

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Son, Hwijae, Jin Woo Jang, Woo Jin Han, and Hyung Ju Hwang. "Sobolev training for physics-informed neural networks." Communications in Mathematical Sciences 21, no. 6 (2023): 1679–705. http://dx.doi.org/10.4310/cms.2023.v21.n6.a11.

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Omar, Sara Ibrahim, Chen Keasar, Ariel J. Ben-Sasson, and Eldad Haber. "Protein Design Using Physics Informed Neural Networks." Biomolecules 13, no. 3 (March 1, 2023): 457. http://dx.doi.org/10.3390/biom13030457.

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The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.
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Dissertations / Theses on the topic "Physics Informed Neural Networks"

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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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Mirzai, Badi. "Physics-Informed Deep Learning for System Identification of Autonomous Underwater Vehicles : A Lagrangian Neural Network Approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301626.

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In this thesis, we explore Lagrangian Neural Networks (LNNs) for system identification of Autonomous Underwater Vehicles (AUVs) with 6 degrees of freedom. One of the main challenges of AUVs is that they have limited wireless communication and navigation under water. AUVs operate under strict and uncertain conditions, where they need to be able to navigate and perform tasks in unknown ocean environments with limited and noisy sensor data. A crucial requirement for localization and adaptive control of AUVs is having an accurate and reliable model of the system’s nonlinear dynamics while taking into account the dynamic environment of the ocean. Most of these dynamics models do not incorporate data. The collection of data for AUVs is difficult, but necessary in order to have more flexibility in the model’s parameters due to the dynamic environment of the ocean. Yet, traditional system identification methods are still dominant today, despite the recent breakthroughs in Deep Learning. Therefore, in this thesis, we aim for a data-driven approach that embeds laws from physics in order to learn the state-space model of an AUV. More precisely, exploring the LNN framework for higher-dimensional systems. Furthermore, we also extend the LNN to account for non-conservative forces acting upon the system, such as damping and control inputs. The networks are trained to learn from simulated data of a second-order ordinary differential equation of an AUV. The trained model is evaluated by integrating paths from different initial states and comparing them to the true dynamics. The results yielded a model capable of predicting the output acceleration of the state space model but struggled in learning the direction of the forward movement with time.
I den här uppsatsen utforskas Lagrangianska Neurala Nätverk (LNN) för systemidentifiering av Autonoma Undervattensfordon (AUV) med 6 frihetsgrader. En av de största utmaningarna med AUV är deras begränsningar när det kommer till trådlös kommunikation och navigering under vatten. Ett krav för att ha fungerande AUV är deras förmåga att navigera och utföra uppdrag under okända undervattensförhållanden med begränsad och brusig sensordata. Dessutom är ett kritiskt krav för lokalisering och adaptiv reglerteknik att ha noggranna modeller av systemets olinjära dynamik, samtidigt som den dynamiska miljön i havet tas i beaktande. De flesta sådana modeller tar inte i beaktande sensordata för att reglera dess parameterar. Insamling av sådan data för AUVer är besvärligt, men nödvändigt för att skapa större flexibilitet hos modellens parametrar. Trots de senaste genombrotten inom djupinlärning är traditionella metoder av systemidentifiering dominanta än idag för AUV. Det är av dessa anledningar som vi i denna uppsats strävar efter en datadriven metod, där vi förankrar lagar från fysik under inlärningen av systemets state-space modell. Mer specifikt utforskar vi LNN för ett system med högre dimension. Vidare expanderar vi även LNN till att även ta ickekonservativa krafter som verkar på systemet i beaktande, såsom dämpning och styrsignaler. Nätverket tränas att lära sig från simulerad data från en andra ordningens differentialekvation som beskriver en AUV. Den tränade modellen utvärderas genom att iterativt integrera fram dess rörelse från olika initialstillstånd, vilket jämförs med den korrekta modellen. Resultaten visade en modell som till viss del var kapabel till att förutspå korrekt acceleration, med begränsad framgång i att lära sig korrekt rörelseriktning framåt i tiden.
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Jing, Li Ph D. Massachusetts Institute of Technology. "Physical symmetry enhanced neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128294.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, February, 2020
Cataloged from student-submitted PDF version of thesis
Includes bibliographical references (pages 91-99).
Artificial Intelligence (AI), widely considered "the fourth industrial revolution", has shown its potential to fundamentally change our world. Today's AI technique relies on neural networks. In this thesis, we propose several physical symmetry enhanced neural network models. We first developed unitary recurrent neural networks (RNNs) that solve gradient vanishing and gradient explosion problems. We propose an efficient parametrization method that requires [sigma] (1) complexity per parameter. Our unitary RNN model has shown optimal long-term memory ability. Next, we combine the above model with a gated mechanism. This model outperform popular recurrent neural networks like long short-term memory (LSTMs) and gated recurrent units (GRUs) in many sequential tasks. In the third part, we develop a convolutional neural network architecture that achieves logarithmic scale complexity using symmetry breaking concepts. We demonstrate that our model has superior performance on small image classification tasks. In the last part, we propose a general method to extend convolutional neural networks' inductive bias and embed other types of symmetries. We show that this method improves prediction performance on lens-distorted image
by Li Jing.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Physics
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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory feedback and the input bias is what produces the divisive decrease in the slope (known as the gain) of the f-I curve. Our work predicts that an increase in noise is required along with increase in inhibitory feedback to attain a divisive and subtractive shift of the gain as seen in experiments [1]. Our results also show that, although the spatial profile of the feedback does not effect the mean activity of the network, it does influence the overall dynamics of the network. Local feedback generates a network oscillation, which is more robust against disruption by noise or uncorrelated input or network heterogeneity, than that for the global feedback (all-to-all coupling) case. For example uncorrelated input completely disrupts the network oscillation generated by global feedback, but only diminishes the network oscillation due to local feedback. This is characterized by 1st and 2nd order spike train statistics. Further, our theory agrees well with numerical simulations of network dynamics.
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Squadrani, Lorenzo. "Deep neural networks and thermodynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Deep learning is the most effective and used approach to artificial intelligence, and yet it is far from being properly understood. The understanding of it is the way to go to further improve its effectiveness and in the best case to gain some understanding of the "natural" intelligence. We attempt a step in this direction with the aim of physics. We describe a convolutional neural network for image classification (trained on CIFAR-10) within the descriptive framework of Thermodynamics. In particular we define and study the temperature of each component of the network. Our results provides a new point of view on deep learning models, which may be a starting point towards a better understanding of artificial intelligence.
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Gyawali, Gaurav. "Solving Atomic Wave Functions Using Artificial Neural Networks." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/honors_theses/104.

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Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the Metropolis algorithm, Variational Monte Carlo (VMC), and Stochastic Reconfiguration (SR) methods for minimization, we were able to obtain a highly accurate ground state wave function for the He atom. To verify our optimization algorithm, we reproduced the results for the ground state of a three dimensional Simple Harmonic Oscillator (SHO) given by Teng [18].
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Düring, Alexander. "Temporal aspects of spin-glass neural networks." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325892.

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Wu, Dawen. "Solving Some Nonlinear Optimization Problems with Deep Learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG083.

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Cette thèse considère quatre types de problèmes d'optimisation non linéaire, à savoir les jeux de bimatrice, les équations de projection non linéaire (NPEs), les problèmes d'optimisation convexe non lisse (NCOPs) et les jeux à contraintes stochastiques (CCGs). Ces quatre classes de problèmes d'optimisation non linéaire trouvent de nombreuses applications dans divers domaines tels que l'ingénierie, l'informatique, l'économie et la finance. Notre objectif est d'introduire des algorithmes basés sur l'apprentissage profond pour calculer efficacement les solutions optimales de ces problèmes d'optimisation non linéaire.Pour les jeux de bimatrice, nous utilisons des réseaux neuronaux convolutionnels (CNNs) pour calculer les équilibres de Nash. Plus précisément, nous concevons une architecture de CNN où l'entrée est un jeu de bimatrice et la sortie est l'équilibre de Nash prédit pour le jeu. Nous générons un ensemble de jeux de bimatrice suivant une distribution de probabilité donnée et utilisons l'algorithme de Lemke-Howson pour trouver leurs véritables équilibres de Nash, constituant ainsi un ensemble d'entraînement. Le CNN proposé est formé sur cet ensemble de données pour améliorer sa précision. Une fois l'apprentissage terminée, le CNN est capable de prédire les équilibres de Nash pour des jeux de bimatrice inédits. Les résultats expérimentaux démontrent l'efficacité computationnelle exceptionnelle de notre approche basée sur CNN, au détriment de la précision.Pour les NPEs, NCOPs et CCGs, qui sont des problèmes d'optimisation plus complexes, ils ne peuvent pas être directement introduits dans les réseaux neuronaux. Par conséquent, nous avons recours à des outils avancés, à savoir l'optimisation neurodynamique et les réseaux neuronaux informés par la physique (PINNs), pour résoudre ces problèmes. Plus précisément, nous utilisons d'abord une approche neurodynamique pour modéliser un problème d'optimisation non linéaire sous forme de système d'équations différentielles ordinaires (ODEs). Ensuite, nous utilisons un modèle basé sur PINN pour résoudre le système d'ODE résultant, où l'état final du modèle représente la solution prédite au problème d'optimisation initial. Le réseau neuronal est formé pour résoudre le système d'ODE, résolvant ainsi le problème d'optimisation initial. Une contribution clé de notre méthode proposée réside dans la transformation d'un problème d'optimisation non linéaire en un problème d'entraînement de réseau neuronal. En conséquence, nous pouvons maintenant résoudre des problèmes d'optimisation non linéaire en utilisant uniquement PyTorch, sans compter sur des solveurs d'optimisation convexe classiques tels que CVXPY, CPLEX ou Gurobi
This thesis considers four types of nonlinear optimization problems, namely bimatrix games, nonlinear projection equations (NPEs), nonsmooth convex optimization problems (NCOPs), and chance-constrained games (CCGs).These four classes of nonlinear optimization problems find extensive applications in various domains such as engineering, computer science, economics, and finance.We aim to introduce deep learning-based algorithms to efficiently compute the optimal solutions for these nonlinear optimization problems.For bimatrix games, we use Convolutional Neural Networks (CNNs) to compute Nash equilibria.Specifically, we design a CNN architecture where the input is a bimatrix game and the output is the predicted Nash equilibrium for the game.We generate a set of bimatrix games by a given probability distribution and use the Lemke-Howson algorithm to find their true Nash equilibria, thereby constructing a training dataset.The proposed CNN is trained on this dataset to improve its accuracy. Upon completion of training, the CNN is capable of predicting Nash equilibria for unseen bimatrix games.Experimental results demonstrate the exceptional computational efficiency of our CNN-based approach, at the cost of sacrificing some accuracy.For NPEs, NCOPs, and CCGs, which are more complex optimization problems, they cannot be directly fed into neural networks.Therefore, we resort to advanced tools, namely neurodynamic optimization and Physics-Informed Neural Networks (PINNs), for solving these problems.Specifically, we first use a neurodynamic approach to model a nonlinear optimization problem as a system of Ordinary Differential Equations (ODEs).Then, we utilize a PINN-based model to solve the resulting ODE system, where the end state of the model represents the predicted solution to the original optimization problem.The neural network is trained toward solving the ODE system, thereby solving the original optimization problem.A key contribution of our proposed method lies in transforming a nonlinear optimization problem into a neural network training problem.As a result, we can now solve nonlinear optimization problems using only PyTorch, without relying on classical convex optimization solvers such as CVXPY, CPLEX, or Gurobi
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Tolley, Emma Elizabeth. "Monte Carlo event reconstruction implemented with artificial neural networks." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65535.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 41).
I implemented event reconstruction of a Monte Carlo simulation using neural networks. The OLYMPUS Collaboration is using a Monte Carlo simulation of the OLYMPUS particle detector to evaluate systematics and reconstruct events. This simulation registers the passage of particles as 'hits' in the detector elements, which can be used to determine event parameters such as momentum and direction. However, these hits are often obscured by noise. Using Geant4 and ROOT, I wrote a program that uses artificial neural networks to separate track hits from noise and reconstruct event parameters. The classification network successfully discriminates between track hits and noise for 97.48% of events. The reconstruction networks determine the various event parameters to within 2-3%.
by Emma Elizabeth Tolley.
S.B.
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Doriat, Aurélien. "Caractérisation des couplages aéro-thermo-mécaniques lors d’un vieillissement par thermo-oxydation de composites à matrice polymère soumis à un écoulement rapide et chauffé." Electronic Thesis or Diss., Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2024. http://www.theses.fr/2024ESMA0018.

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Les matériaux composites à matrice organique renforcés de fibres de carbone (CFRP) sont largement utilisés dans les structures aéronautiques froides. Dans les applications de moteurs aéronautiques, comme les aubes de FAN, ces matériaux peuvent être soumis à des conditions environnementales particulièrement sévères, avec des températures pouvant atteindre 120°C et une vitesse d’écoulement proche de Mach 1.Il est bien établi que les polymères époxy sont sujets à des phénomènes de thermo-oxydation lorsqu’ils sont exposés à des températures élevées. Ce phénomène implique la diffusion et la réaction de l’oxygène au sein du polymère, entraînant des changements de couleur, une antiplasticisation du matériau, une fragilisation. Jusqu’à présent, les essais de vieillissement ont été principalement effectués dans des fours à air statique, fournissant une compréhension détaillée du phénomène dans ces conditions. Cependant, l’impact de l’écoulement d’air sur la thermo-oxydation reste à explorer. Cette étude vise ainsi à approfondir la compréhension du couplage entre l’écoulement d’air et la dégradation du matériau par thermo-oxydation. Des échantillons ont été vieillis dans un four sous air à pression atmosphérique et dans la soufflerie BATH, adaptée pour ces essais et capable de générer un écoulement d’air à plus de 150 ◦C et Mach 1, reproduisant ainsi les conditions d’usage les plus sévère rencontrées dans des moteurs d’avion. Cette comparaison entre essai en four et soufflerie a montré une accélération du vieillissement en soufflerie. Pour obtenir ce résultat, une technique expérimentale basée sur le changement de couleur induit par l’oxydation a été développée et utilisée. Cette technique a été validée avec des essais d’indentation. Avec cette meilleure compréhension de l’accélération du vieillissement, un modèle couplé entre l’écoulement, la chimie de l’oxydation et les changements de propriétés mécaniques a été mis en place afin de mieux comprendre les mécanismes à l’interface. Cette modélisation comprends trois étapes. Les champs de pression et de température à la surface de l’échantillon ont été calculés par simulation fluide moyennée (RANS). Puis, un modèle mécanistique a été utilisé décrivant les réactions chimiques lors de l’oxydation. Enfin, sur la base des mesures de couleur, un réseau de neurones informé par la physique (PINN) a été mis en place pour coupler les quantités chimiques aux propriétés mécaniques
Carbon fiber-reinforced polymer matrix composites (CFRP) are widely used in cold aeronautical structures. In aeronautical engine applications, such as fan blades, these materials can be subjected to particularly severe environmental conditions, with temperatures reaching up to 120 ◦C and airflow speeds close to Mach 1. It is well established that epoxy polymers are prone to thermo-oxidation phenomena when exposed to high temperatures.This phenomenon involves the diffusion and reaction of oxygen within the polymer, leading to color changes, antiplasticization of the material, and embrittlement. Until now, aging tests have been mainly conducted in static air ovens, providing a detailed understanding of the phenomenon under these conditions. However, the impact of airflow on thermo-oxidation remains to be explored.This study thus aims to deepen the understanding of the coupling between airflow and material degradation due to thermo-oxidation.Samples were aged in an oven under air at atmospheric pressure and in the BATH wind tunnel, adapted for these tests and capable of generating an airflow at over 150 ◦C and Mach 1, thereby reproducing the most severe usage conditions encountered in aircraft engines. This comparison between oven and wind tunnel tests showed an acceleration of aging in the wind tunnel. To achieve this result, an experimental technique based on the color change induced by oxidation was developed and used. This technique was validated with indentation tests. With this improved understanding of the accelerated aging, a coupled model between the airflow, oxidation chemistry, and changes in mechanical properties was established to better understand the interfacial mechanisms. This modeling comprises three steps. The pressure and temperature fields at the sample surface were calculated using Reynolds-Averaged Navier-Stokes (RANS) fluid simulations. Then, a mechanistic model was used to describe the chemical reactions during oxidation. Finally, based on thecolor measurements, a physics-informed neural network (PINN) was implemented to couple the chemical quantities to the mechanical properties
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Books on the topic "Physics Informed Neural Networks"

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Aubin, Jean Pierre. Neural networks and qualitative physics. Cambridge: Cambridge University Press, 1996.

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Aubin, Jean Pierre. Neural networks and qualitative physics. Cambridge: Cambridge University Press, 2011.

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E, Domany, and Cowan J, eds. Models of Neural Networks IV.: The Visual System - Physics of Neural Networks. S. l: Springer-Verlag New York, Incorporated, 2002.

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Murray, Alan F. Applications of Neural Networks. Boston, MA: Springer US, 1995.

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Zhang, Xiang-Sun. Neural Networks in Optimization. Boston, MA: Springer US, 2000.

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E, Golès, Martínez Servet, and School on Statistical Physics and Cooperative Systems (2nd : 1990 : Santiago, Chile), eds. Statistical physics, automata networks, and dynamical systems. Dordrecht: Kluwer Academic Publishers, 1992.

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Pham, Duc Truong. Neural Networks for Identification, Prediction and Control. London: Springer London, 1995.

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Akhmet, Marat, and Mehmet Onur Fen. Replication of Chaos in Neural Networks, Economics and Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47500-3.

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Delgado-Frias, José G. VLSI for Artificial Intelligence and Neural Networks. Boston, MA: Springer US, 1991.

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Workshop on Neural Networks: from Biology to High Energy Physics (2nd 1992 Isola d'Elba, Italy). Second Workshop on Neural Networks: from Biology to High Energy Physics, Isola d'Elba, Italy, June 18-26, 1992. Edited by Benhar Omar. Singapore: World Scientific, 1993.

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Book chapters on the topic "Physics Informed Neural Networks"

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Kollmannsberger, Stefan, Davide D’Angella, Moritz Jokeit, and Leon Herrmann. "Physics-Informed Neural Networks." In Deep Learning in Computational Mechanics, 55–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76587-3_5.

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Awojoyogbe, Bamidele O., and Michael O. Dada. "Physics Informed Neural Networks (PINNs)." In Series in BioEngineering, 33–47. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6370-2_2.

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Goswami, Somdatta, Aniruddha Bora, Yue Yu, and George Em Karniadakis. "Physics-Informed Deep Neural Operator Networks." In Computational Methods in Engineering & the Sciences, 219–54. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36644-4_6.

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Anitescu, Cosmin, Burak İsmail Ateş, and Timon Rabczuk. "Physics-Informed Neural Networks: Theory and Applications." In Computational Methods in Engineering & the Sciences, 179–218. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36644-4_5.

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Wang, Sifan, and Paris Perdikaris. "Adaptive Training Strategies for Physics-Informed Neural Networks." In Knowledge-Guided Machine Learning, 133–60. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-6.

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de Wolff, Taco, Hugo Carrillo, Luis Martí, and Nayat Sanchez-Pi. "Optimal Architecture Discovery for Physics-Informed Neural Networks." In Advances in Artificial Intelligence – IBERAMIA 2022, 77–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22419-5_7.

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Kim, Hyea Hyun, and Hee Jun Yang. "Domain Decomposition Algorithms for Physics-Informed Neural Networks." In Domain Decomposition Methods in Science and Engineering XXVI, 697–704. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95025-5_76.

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Johnson, Rob, Soukaïna Filali Boubrahimi, Omar Bahri, and Shah Muhammad Hamdi. "Physics-Informed Neural Networks for Solar Wind Prediction." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 273–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37731-0_21.

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Turinici, Gabriel. "Optimal Time Sampling in Physics-Informed Neural Networks." In Lecture Notes in Computer Science, 218–33. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78395-1_15.

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Vemuri, Sai Karthikeya, Tim Büchner, Julia Niebling, and Joachim Denzler. "Functional Tensor Decompositions for Physics-Informed Neural Networks." In Lecture Notes in Computer Science, 32–46. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78389-0_3.

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Conference papers on the topic "Physics Informed Neural Networks"

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Miao, Yuyang, Haolin Li, and Danilo Mandic. "GPINN: Physics-Informed Neural Network with Graph Embedding." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651053.

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Zhang, Xin, Nanxi Chen, Jiyan Qiu, Pengcheng Shi, Xuesong Wu, and Wu Yuan. "Importance-Guided Sequential Training for Physics-Informed Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651329.

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Yokota, Kazuya, Masataka Ogura, Takahiko Kurahashi, and Masajiro Abe. "Physics-Informed CNN for the Design of Acoustic Equipment." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650136.

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Navarin, Nicolò, Paolo Frazzetto, Luca Pasa, Pietro Verzelli, Filippo Visentin, Alessandro Sperduti, and Cesare Alippi. "Physics-Informed Graph Neural Cellular Automata: an Application to Compartmental Modelling." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–9. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650578.

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Mishra, Sourav, Rudrashis Majumder, and Suresh Sundaram. "PINGS: Physics Informed Networks with Guided Supermasks for Sequential PDE Solving." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651548.

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Cao, Yuandong, Chi Chiu So, Junmin Wang, and Siu Pang Yung. "System Stabilization of PDEs using Physics-Informed Neural Networks (PINNs)." In 2024 43rd Chinese Control Conference (CCC), 8759–64. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662626.

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Costa, Nuno, Filipa S. Barros, João J. G. Lima, Rui F. Pinto, and André Restivo. "Leveraging Physics-Informed Neural Networks as Solar Wind Forecasting Models." In ESANN 2024, 425–30. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-110.

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Bai, Yidi, Xinhai Chen, Chunye Gong, and Jie Liu. "ImPINN: Improved Physics-informed neural networks for solving inverse problems." In 2024 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 189–98. IEEE, 2024. https://doi.org/10.1109/cyberc62439.2024.00041.

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Wei, R., J. Chen, Q. Wu, H. Ren, and L. Zhong. "Plasma chemical kinetic simulation based on physics-informed neural networks." In 2024 IEEE International Conference on Plasma Science (ICOPS), 1. IEEE, 2024. http://dx.doi.org/10.1109/icops58192.2024.10627060.

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Ooi, Chin Chun, Anran Huang, Zhao Wei, Shiyao Qin, Jian Cheng Wong, Pao-Hsiung Chiu, and My Ha Dao. "Importance of Nyquist-Shannon Sampling in Training of Physics-Informed Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650694.

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Reports on the topic "Physics Informed Neural Networks"

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Nadiga, Balasubramanya, and Robert Lowrie. Physics Informed Neural Networks as Computational Physics Emulators. Office of Scientific and Technical Information (OSTI), June 2023. http://dx.doi.org/10.2172/1985825.

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Guan, Jiajing, Sophia Bragdon, and Jay Clausen. Predicting soil moisture content using Physics-Informed Neural Networks (PINNs). Engineer Research and Development Center (U.S.), August 2024. http://dx.doi.org/10.21079/11681/48794.

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Environmental conditions such as the near-surface soil moisture content are valuable information in object detection problems. However, such information is generally unobtainable at the necessary scale without active sensing. Richards’ equation is a partial differential equation (PDE) that describes the infiltration process of unsaturated soil. Solving the Richards’ equation yields information about the volumetric soil moisture content, hydraulic conductivity, and capillary pressure head. However, Richards’ equation is difficult to approximate due to its nonlinearity. Numerical solvers such as finite difference method (FDM) and finite element method (FEM) are conventional in approximating solutions to Richards’ equation. But such numerical solvers are time-consuming when used in real-time. Physics-informed neural networks (PINNs) are neural networks relying on physical equations in approximating solutions. Once trained, these networks can output approximations in a speedy manner. Thus, PINNs have attracted massive attention in the numerical PDE community. This project aims to apply PINNs to the Richards’ equation to predict underground soil moisture content under known precipitation data.
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Ellis, Kai, Nilanjan Banerjee, and Christopher Pierce. Modeling a Thermionic Electron Source Using a Physics-Informed Neural Network. Office of Scientific and Technical Information (OSTI), October 2023. http://dx.doi.org/10.2172/2008057.

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D'Elia, Marta, Michael L. Parks, Guofei Pang, and George Karniadakis. nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1614899.

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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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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.
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Wells, Daniel, Benjamin Baker, and Kristine Pankow. The Feasibility of Incorporating a 3D Velocity Model Into Earthquake Location Around Salt Lake City, UT Using a Physics Informed Neural Network. Office of Scientific and Technical Information (OSTI), August 2023. http://dx.doi.org/10.2172/2430497.

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Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, November 2024. http://dx.doi.org/10.31979/mti.2024.2365.

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The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected and refined comprehensive data from Caltrans project expenditures from 1983 to 2021, ensuring a high-quality dataset. Subject matter experts validated the data, enhancing its reliability. Two models were developed: a statistical model using exponential regression to account for non-linear cost growth, and an AI model employing neural networks to handle complex relationships in the data. Model performance was evaluated based on accuracy and reliability through repeated testing and validation. Key findings indicated that the new models significantly improved the precision of cost forecasts, reducing the variance between predicted and actual project costs. This advancement minimizes budget overruns and enhances resource allocation efficiency. Additionally, leveraging historical data with current market trends refined the models’ predictive power, boosting stakeholder confidence in project budgeting and financial planning. The study’s innovative approach, integrating machine learning and big data analytics, transforms traditional estimation practices and serves as a reference for other state highway programs. Continuous improvement and broader application of these models are recommended to further enhance cost estimation accuracy and support informed decision-making in transportation infrastructure management.
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Perdigão, Rui A. P. Neuro-Quantum Cyber-Physical Intelligence (NQCPI). Synergistic Manifolds, October 2024. http://dx.doi.org/10.46337/241024.

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Neuro-Quantum Cyber-Physical Intelligence (NQCPI) is hereby introduced, entailing a novel framework for nonlinear natural-based neural post-quantum information physics, along with novel advances in far-from-equilibrium thermodynamics and evolutionary cognition in post-quantum neurobiochemistry for next-generation information physical systems intelligence. NQCPI harnesses and operates with the higher-order nonlinear nature of previously elusive quantum behaviour, including in open chaotic dissipative systems in thermodynamically and magneto-electrodynamically disruptive conditions, such as in natural biological and environmental systems, thereby paving new pathways for post-quantum information technologies, including new paradigms for information encoding, encryption, transmission and security elusive to SoA post-quantum approaches. NQCPI further harnesses and operates with novel quantum properties including new classes of high-order emergence and entanglement structures, new neuro-quantum physical properties, with higher-order post-quantum-proof improvements in security, storage and relaying of information. It further empowers new capabilities to disarm security protocols of adversarial powers including those already at SoA quantum and post-quantum levels. This new technology is implemented into a novel coevolutionary system-of-systems seamlessly operating across classical, quantum, post-quantum cryptographic and key distribution approaches, thereby generalising them with added value whilst ensuring backward compatibility for seamless articulation with legacy and SoA protocols. Having demonstrated disruptive added value relative to post-quantum security, NQCPI is also tested and implemented in other quantum technological developments, ranging from sensing to communication and computation, in articulation with QITES (Quantum Information Technologies in the Earth Sciences), QuASI (Quantum Aerospace Systems Intelligence), AIPSI (Augmented Information Physical Systems Intelligence), and Synergistic Nonlinear Quantum Wave Intelligence Networks (SyNQ-WIN), respectively from Perdigão (2020, 2023) and Perdigão and Hall (2023, 2024). Perdigão, R.A.P. (2020): QITES - Quantum Information Technologies in the Earth Sciences. https://doi.org/10.46337/qites.200628 Perdigão, R.A.P. (2023): QuASI - Quantum Aerospace Systems Intelligence. https://doi.org/10.46337/quasi.230901 Perdigão, R.A.P.; Hall, J. (2023): Augmented Information Physical Systems Intelligence (AIPSI). https://doi.org/10.46337/230414 Perdigão, R.A.P.; Hall, J. (2024): Synergistic Nonlinear Quantum Wave Intelligence Networks (SyNQ-WIN). https://doi.org/10.46337/240118
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SECOND-ORDER ANALYSIS OF BEAM-COLUMNS BY MACHINE LEARNING-BASED STRUCTURAL ANALYSIS THROUGH PHYSICS-INFORMED NEURAL NETWORKS. The Hong Kong Institute of Steel Construction, December 2023. http://dx.doi.org/10.18057/ijasc.2023.19.4.10.

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The second-order analysis of slender steel members could be challenging, especially when large deflection is involved. This paper proposes a novel machine learning-based structural analysis (MLSA) method for second-order analysis of beam-columns, which could be a promising alternative to the prevailing solutions using over-simplified analytical equations or traditional finite-element-based methods. The effectiveness of the conventional machine learning method heavily depends on both the qualitative and the quantitative of the provided data. However, such data are typically scarce and expensive to obtain in structural engineering practices. To address this problem, a new and explainable machine learning-based method, named Physics-informed Neural Networks (PINN), is employed, where the physical information will be utilized to orientate the learning process to create a self-supervised learning procedure, making it possible to train the neural network with few or even no predefined datasets to achieve an accurate approximation. This research extends the PINN method to the problems of second-order analysis of steel beam-columns. Detailed derivations of the governing equations, as well as the essential physical information for the training process, are given. The PINN framework and the training procedure are provided, where an adaptive loss weight control algorithm and the transfer learning technic are adopted to improve numerical efficiency. The practicability and accuracy of which are validated by four sets of verification examples.
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