Results 21 to 30 of about 2,291 (214)
Sensors, 2023 This paper proposes a physics-informed neural network (PINN) for predicting the early-age time-dependent behaviors of prestressed concrete beams. The PINN utilizes deep neural networks to learn the time-dependent coupling among the effective prestress ...
Hyun-Woo Park, Jin-Ho Hwang
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Current density impedance imaging with PINNs
CoRR, 2023 In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization. This method constructs a physics-informed loss function by merging the regularized least-squares output functional with an underlying differential equation, which describes the relationship between the ...
Chenguang Duan +3 more
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Unified relativistic description of piNN and gamma-piNN
, 1998 We present a unified description of the relativistic piNN and gamma-piNN systems where the strong interactions are described non-perturbatively by four-dimensional integral equations. A feature of our approach is that the photon is coupled in all possible ways to the strong interaction contributions.
Kvinikhidze, A. N., Blankleider, B.
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PHYSICS-INFORMED NEURAL NETWORKS FOR NARROWBAND SIGNAL PROPAGATION MODELING
Електроніка та інформаційні технологіїBackground. Physics-informed neural networks (PINN) demonstrated strong capabilities in solving direct and inverse problems for partial differential equations.
Igor Kolych
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TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
Computer Methods in Applied Mechanics and EngineeringWe introduce the Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN) for accomplishing nonlinear model order reduction (MOR) of transport-dominated partial differential equations in an MOR-integrating PINNs framework. Building on the recent development of the GPT-PINN that is a network-of-networks design achieving snapshot ...
Yanlai Chen +3 more
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Unified model of the relativistic piNN and gamma-piNN systems
, 1998 Contribution to Proceedings, APCTP Workshop on Astro-Hadron Physics "Properties of Hadrons in Matter", Seoul, Korea, 25-31 October 1997, to be published by World Scientific.
Blankleider, B., Kvinikhidze, A. N.
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Results in Applied MathematicsPartial differential equations (PDEs) are essential for modeling a wide range of physical phenomena. Physics-Informed Neural Networks (PINNs) offer a promising numerical framework for solving PDEs, but their performance often depends on the choice of ...
Nursyiva Irsalinda +4 more
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A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature
IEEE Access, 2022 The physics-informed neural network (PINN) has drawn much attention as it can reduce training data size and eliminate the need for physics equation identification.
Gyouho Cho +4 more
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A robust Gated-PINN to resolve local minima issues in solving differential algebraic equations
Results in EngineeringPhysics-Informed Neural Network (PINN) has emerged as a promising tool for solving various physical problems with differential equations. However in practice, PINN often suffers from the local minima issue while solving problems with minimal initial ...
SangJoon Lee, Byung-Tak Lee, Seok Kap Ko
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PINN-Based Method for Predicting Flow Field Distribution of the Tight Reservoir after Fracturing
Geofluids, 2022 The physical-informed neural network (PINN) model can greatly improve the ability to fit nonlinear data with the incorporation of prior knowledge, which endows traditional neural networks with interpretability.
Jun Pu +5 more
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