Results 31 to 40 of about 8,116 (238)
RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations [PDF]
, 2022 This work aims to provide an effective deep learning framework to predict the vector-soliton solutions of the coupled nonlinear equations and their interactions.
Li, Min +3 more
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Nuclear Engineering and Technology, 2023 The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an ...
Gibeom Kim, Gyunyoung Heo
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Mathematics, 2023 In practical engineering applications, there is a high demand for inverting parameters for various materials, and obtaining monitoring data can be costly.
Meijun Zhou, Gang Mei
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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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RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows [PDF]
, 2023 Physics-informed neural networks (PINNs) provide a framework to build surrogate models for dynamical systems governed by differential equations. During the learning process, PINNs incorporate a physics-based regularization term within the loss function ...
Ghosh, Shinjan +3 more
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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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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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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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