Results 161 to 170 of about 2,349 (245)

Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)

We propose a new physics-informed neural network framework, IDPINN, based on the enhancement of initialization and domain decomposition to improve prediction accuracy.
Si, Chenhao, Yan, Ming
core  

Generative Inverse Design of Metamaterials Enhanced by Physics-Informed Neural Network

X215X217Metamaterial design traditionally depends on computationally expensive physics-based simulations, while deep learning approaches require extensive, high-quality training data. This work introduces a data-free deep learning framework that combines
Erdmann, Andreas, Medvedev, Vlad, Roßkopf, Andreas   +2 more
core   +1 more source

Surfactant representation using COSMO screened charge density for adsorption isotherm prediction using Physics-Informed Neural Network (PINN). [PDF]

J Cheminform
Alimin AA, Srasamran K, Yuenyong W, Charoensaeng A, Shiau BJ, Suriyapraphadilok U.   +5 more
europepmc   +1 more source

Learning variable-order time fractional diffusion equations using Physics-Informed Neural Networks. [PDF]

PLoS One
Ren L, Jin S.
europepmc   +1 more source

PRECISION AGRICULTURE ENHANCED WITH PHYSICS-INFORMED NEURAL NETWORKS (PINNS)

International Journal of Applied Mathematics
Precision agriculture boosts harvests and safeguards the soil by managing water, nutrients, and earth with exacting care—like drip lines feeding each plant a slow, perfect trickle. In this study, we introduce one unified method that blends Physics-Informed Neural Networks (PINNs) with an IoT-driven precision farming system, where fingertip-sized soil ...
openaire   +1 more source

Residual resampling-based physics-informed neural network for neutron diffusion equations

The neutron diffusion equation plays a pivotal role in the analysis of nuclear reactors. Nevertheless, employing the Physics-Informed Neural Network (PINN) method for its solution entails certain limitations.
He, Yun-Ling, Xiang, Di, Yao, He-Min, Hang, Qin, Zhang, Heng, Liu, Dong   +5 more
core  

PINN for stiff moving-boundary PDE to predict the locking point in superheated steam drying. [PDF]

Sci Rep
Malekjani N, Bück A, Tsotsas E, Kharaghani A.   +3 more
europepmc   +1 more source

Critical heat flux prediction by ensemble and physics-informed neural networks [PDF]

LAUREA MAGISTRALEIl Critical Heat Flux (CHF) si riferisce alla transizione tra nucleate boiling e film boiling. Questa transizione porta a una significativa riduzione dell'efficienza di trasferimento del calore.
Gatti, Irene
core  

Physics-informed differentiable solvers for learning parametric solution manifolds in heterogeneous physical systems. [PDF]

PNAS Nexus
Panahi M, Porta GM, Riva M, Guadagnini A.   +3 more
europepmc   +1 more source

Hybrid Physics-Informed and Bayesian Modeling of Single-Nanoparticle-Cell Adhesion Kinetics under Cytoskeletal Perturbation. [PDF]

Comput Struct Biotechnol J
Bettahar H, Santos HA, Zhou Q.
europepmc   +1 more source