Results 11 to 20 of about 8,116 (238)
Results in Physics, 2023 In this paper, by modifying loss function MSE and training area of the physics-informed neural network (PINN), we proposed two neural network models: mix-training PINN and prior information mix-training PINN.
Shifang Tian, Chenchen Cao, Biao Li
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A PINN framework for inverse physical design of metal-loaded electromagnetic devices
AIP AdvancesTo solve the difficulty of inverse design of metal-loaded electromagnetic devices, we propose a physics-informed neural network (PINN) framework. With the emergence of PINNs, some scholars within the field of electromagnetism have utilized them to design
Yu-Hang Liu +3 more
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TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs [PDF]
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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DDR-PINN: A Dynamic Domain–Gradient Reweighting Physics-Informed Neural Network
Applied SciencesPhysics-informed neural networks (PINNs) solve partial differential equations (PDEs) by embedding physical conditions as soft penalties into the loss function.
Shangpeng Lei +5 more
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Results in Control and OptimizationThe spread of invasive species poses a significant challenge to native biodiversity and ecosystem stability. An optimal control strategies to minimize the negative impacts of invasive species populations on native species and the ecosystem must be done ...
Yudi Ari Adi +5 more
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, 2021 Drawing on literature along with the visual and performing arts, Anthony B. Pinn theorizes religion as a technology for interrogating human experiences understanding the ways in which things are always involved in processes of exchange and ...
Anthony B. Pinn +2 more
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Inclusion, multiculturalism and school connection: reconsidering teacher education through student’s video generation [PDF]
, 2023 This paper presents a reflection on the necessity of designing inclusive educational practices, not only at school, but at Education Faculties, too.
García-Sampedro Fernández-Canteli, Marta +2 more
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Physics-Informed Neural Network-Enabled Forward Prediction and Inverse Design of Ring Origami. [PDF]
Adv Sci (Weinh)This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Ning L, Lu L, Leanza S, Zhao RR.
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On physics-informed neural networks for quantum computers
Frontiers in Applied Mathematics and Statistics, 2022 Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.
Stefano Markidis
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