Imagen de Portada

Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ehlers, Svenja, Wagner, Niklas A., Scherzl, Annamaria, Klein, Marco, Hoffmann, Norbert, Stender, Merten
Formato:	Preprint
Publicado:	2024
Materias:	Fluid Dynamics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves
por: Ehlers, Svenja, et al.
Publicado: (2025)

Bridging ocean wave physics and deep learning: Physics-informed neural operators for nonlinear wavefield reconstruction in real-time
por: Ehlers, Svenja, et al.
Publicado: (2025)

Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh-Bénard convection
por: Mommert, Michael, et al.
Publicado: (2024)

Equation identification for fluid flows via physics-informed neural networks
por: New, Alexander, et al.
Publicado: (2024)

An analysis and solution of ill-conditioning in physics-informed neural networks
por: Cao, Wenbo, et al.
Publicado: (2024)

Inference of water waves surface elevation from horizontal velocity components using physics informed neural networks (PINN)
por: Sallam, Omar, et al.
Publicado: (2024)

Active grid turbulence anomalies through the lens of physics informed neural networks
por: Angriman, Sofía, et al.
Publicado: (2024)

Learning thermoacoustic interactions in combustors using a physics-informed neural network
por: Mariappan, Sathesh, et al.
Publicado: (2023)

Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations
por: Xiang, Zixue, et al.
Publicado: (2021)

Hard-constrained neural networks for modelling nonlinear acoustics
por: Ozan, Defne Ege, et al.
Publicado: (2023)

ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
por: Thakur, Sukirt, et al.
Publicado: (2022)

Inferring viscoplastic models from velocity fields: a physics-informed neural network approach
por: Lardy, Martin, et al.
Publicado: (2025)

Finite difference physics-informed neural networks enable improved solution accuracy of the Navier-Stokes equations
por: Roy, Nityananda, et al.
Publicado: (2024)

Discontinuity-aware KAN-based physics-informed neural networks
por: Lei, Guoqiang, et al.
Publicado: (2025)

Mixed data-source transfer learning for a turbulence model augmented physics-informed neural network
por: Toma, Christian, et al.
Publicado: (2026)

A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation
por: Cao, Wenbo, et al.
Publicado: (2024)

MSPINN: Multiple scale method integrated physics-informed neural networks for reconstructing transient natural convection
por: Ohashi, Nagahiro, et al.
Publicado: (2024)

PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
por: Dreisbach, Maximilian, et al.
Publicado: (2024)

Inferring parameters and reconstruction of two-dimensional turbulent flows with physics-informed neural networks
por: Parfenyev, Vladimir, et al.
Publicado: (2024)

Tail observability and fourth-order closure recovery in physics-informed neural networks for Bhatnagar-Gross-Krook normal shocks
por: Roohi, Ehsan
Publicado: (2026)

A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks
por: Cao, Wenbo, et al.
Publicado: (2024)

Turbulence model augmented physics informed neural networks for mean flow reconstruction
por: Patel, Yusuf, et al.
Publicado: (2023)

Learning of viscosity functions in rarefied gas flows with physics-informed neural networks
por: Tucny, Jean-Michel, et al.
Publicado: (2023)

Quantifying local and global mass balance errors in physics-informed neural networks
por: Mamud, Md Lal, et al.
Publicado: (2023)

Parametric and inverse analysis of flow inside an obstructed channel under the influence of magnetic field using physics informed neural networks
por: Ghaderi, Ehsan, et al.
Publicado: (2024)

An unstructured adaptive mesh refinement for steady flows based on physics-informed neural networks
por: Zhu, Yongzheng, et al.
Publicado: (2024)

Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
por: Qian, Yixiao, et al.
Publicado: (2026)

A matrix preconditioning framework for physics-informed neural networks based on adjoint method
por: Song, Jiahao, et al.
Publicado: (2025)

Physics-informed neural networks for unsteady incompressible flows with time-dependent moving boundaries
por: Zhu, Yongzheng, et al.
Publicado: (2023)

Solving compressible Navier-Stokes equations using the feature-enhanced neural network
por: Song, Jiahao, et al.
Publicado: (2026)

Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
por: Ehlers, Svenja, et al.
Publicado: (2023)

Physics-informed neural networks for multi-field visualization with single-color laser induced fluorescence
por: Ohashi, Nagahiro, et al.
Publicado: (2024)

Determination of Navier's slip parameter using data assimilation
por: Jarolímová, Alena, et al.
Publicado: (2024)

Research on the identification of the two-phase flow pattern of gas-liquid in a vertical rising tube based on BP neural networks
por: Zhang, Xiaojun, et al.
Publicado: (2024)

A physically-informed sea spray generation model for splashing waves
por: Tang, Kaitao, et al.
Publicado: (2025)

Reconstruction of instantaneous flow fields from transient velocity snapshots using physics-informed neural networks: Applications to pulsatile blood flow behind a stenosis
por: Ueda, Kakeru, et al.
Publicado: (2026)

A deep neural network physics-based reduced order model for dynamic stall
por: Baldan, Giacomo, et al.
Publicado: (2024)

FENN: Feature-enhanced neural network for solving partial differential equations involving fluid mechanics
por: Song, Jiahao, et al.
Publicado: (2025)

Discontinuity-aware physics-informed neural network for phase-field method in three-phase flow with phase change
por: Lei, Guoqiang, et al.
Publicado: (2025)

Large-eddy simulation nets (LESnets) based on physics-informed neural operator for wall-bounded turbulence
por: Zhao, Sunan, et al.
Publicado: (2026)