Results 91 to 100 of about 2,349 (245)

Using NeuralPDE.jl to solve differential equations

Discrete and Continuous Models and Applied Computational Science
This paper describes the application of physics-informed neural network (PINN) for solving partial derivative equations. Physics Informed Neural Network is a type of deep learning that takes into account physical laws to solve physical equations more ...
Daria M. Belicheva, Ekaterina A. Demidova, Kristina A. Shtepa, Migran N. Gevorkyan, Anna V. Korolkova, Dmitry S. Kulyabov   +5 more
doaj   +1 more source

Nordgren PINNs to VQE: Advancing Hydraulic Fracturing Simulations in Shale Reservoirs

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT This study advances hydraulic fracturing simulations in shale reservoirs using two computational paradigms, Physics‐Informed Neural Networks (PINNs) and the Variational Quantum Eigensolver (VQE). PINNs were employed to solve Nordgren's equation, which governs fracture width evolution, by embedding physical laws into the neural network ...
Dennis Delali Kwesi Wayo, Sonny Irawan, Mohd Zulkifli Bin Mohamad Noor, Mudasar Zafar, Shynar Juziyeva, Camila Martins Saporetti, Leonardo Goliatt, Randy Hazlett   +7 more
wiley   +1 more source

Enhanced PINNs for data-driven solitons and parameter discovery for (2+ 1)-dimensional coupled nonlinear Schrödinger systems

Results in Applied Mathematics
This paper investigates data-driven solutions and parameter discovery to (2+ 1)-dimensional coupled nonlinear Schrödinger equations with variable coefficients (VC-CNLSEs), which describe transverse effects in optical fiber systems under perturbed ...
Hamid Momeni, AllahBakhsh Yazdani Cherati, Ali Valinejad   +2 more
doaj   +1 more source

KP-PINNs: Kernel Packet Accelerated Physics Informed Neural Networks

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Differential equations are involved in modeling many engineering problems. Many efforts have been devoted to solving differential equations. Due to the flexibility of neural networks, Physics Informed Neural Networks (PINNs) have recently been proposed to solve complex differential equations and have demonstrated superior performance in many ...
Siyuan Yang, Cheng Song, Zhilu Lai, Wenjia Wang   +3 more
openaire   +2 more sources

Physics-informed neural networks for inviscid transonic flows around an airfoil

Physics-informed neural networks (PINNs) have gained popularity as a deep-learning-based parametric partial differential equation solver. Especially for engineering applications, this approach is promising because a single neural network (NN) could ...
Wassing, Simon, Bekemeyer, Philipp, Langer, Stefan   +2 more
core   +1 more source

Physics‐Informed Neural Networks for Battery Degradation Prediction Under Random Walk Operations

Quality and Reliability Engineering International, EarlyView.
ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim, Huadong Mo, Hemanshu Pota, Daoyi Dong   +3 more
wiley   +1 more source

$Pinn - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks

37 pages, 22 ...
Vicens Figueres, Júlia, Vanderhaeghen, Juliette, Bragone, Federica, Morozovska, Kateryna, Shukla, Khemraj   +4 more
openaire   +3 more sources

Artificial Intelligence for Blue Transformation: A Review of Predictive Modeling and Decision Support Systems in Sustainable Aquaculture

Sustainable Development, EarlyView.
ABSTRACT The FAO's “blue transformation” roadmap necessitates a fundamental shift towards precision aquaculture to meet global food security targets while minimizing environmental footprints. This review provides a comprehensive overview of how artificial intelligence (AI) and decision support systems (DSS) serve as pivotal enablers for the “better ...
Mustafa Öz, Enes Üstüner
wiley   +1 more source

Feature Mapping in Physics-Informed Neural Networks (PINNs)

In this paper, the training dynamics of PINNs with a feature mapping layer via the limiting Conjugate Kernel and Neural Tangent Kernel is investigated, shedding light on the convergence of PINNs; Although the commonly used Fourier-based feature mapping has achieved great success, we show its inadequacy in some physics scenarios.
Zeng, Chengxi, Burghardt, Tilo, Gambaruto, Alberto M   +2 more
openaire   +2 more sources

Computational intelligence model for predicting the compressive strength of FRP‐confined concrete column

Structural Concrete, EarlyView.
Abstract Fiber reinforced polymer (FRP) wrapping technology is commonly used to enhance the compressive strength (CS) of reinforced concrete (RC) members. Accurate prediction of the compressive strength of FRP‐confined concrete columns is crucial for optimizing structural design and helps reduce the time and costs associated with physical testing ...
XuanRui Yu, Nima Khodadadi, Anxiang Song, Tianyu Hu, Yang Yu, Antonio Nanni   +5 more
wiley   +1 more source