Quantifying local and global mass balance errors in physics-informed neural networks
Fuente:
arXiv
Saved in:
| Main Authors: | Mamud, Md Lal, Mudunuru, Maruti K., Karra, Satish, Ahmmed, Bulbul |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Discontinuity-aware KAN-based physics-informed neural networks
by: Lei, Guoqiang, et al.
Published: (2025)
by: Lei, Guoqiang, et al.
Published: (2025)
Turbulence model augmented physics informed neural networks for mean flow reconstruction
by: Patel, Yusuf, et al.
Published: (2023)
by: Patel, Yusuf, et al.
Published: (2023)
Learning of viscosity functions in rarefied gas flows with physics-informed neural networks
by: Tucny, Jean-Michel, et al.
Published: (2023)
by: Tucny, Jean-Michel, et al.
Published: (2023)
An unstructured adaptive mesh refinement for steady flows based on physics-informed neural networks
by: Zhu, Yongzheng, et al.
Published: (2024)
by: Zhu, Yongzheng, et al.
Published: (2024)
Finite difference physics-informed neural networks enable improved solution accuracy of the Navier-Stokes equations
by: Roy, Nityananda, et al.
Published: (2024)
by: Roy, Nityananda, et al.
Published: (2024)
Discontinuity-aware physics-informed neural network for phase-field method in three-phase flow with phase change
by: Lei, Guoqiang, et al.
Published: (2025)
by: Lei, Guoqiang, et al.
Published: (2025)
Fine-tuning physics-informed neural networks for cavity flows using coordinate transformation
by: Takao, Ryuta, et al.
Published: (2025)
by: Takao, Ryuta, et al.
Published: (2025)
KH-PINN: Physics-informed neural networks for Kelvin-Helmholtz instability with spatiotemporal and magnitude multiscale
by: Wu, Jiahao, et al.
Published: (2024)
by: Wu, Jiahao, et al.
Published: (2024)
Physics-informed neural networks for solving moving interface flow problems using the level set approach
by: Mullins, Mathieu, et al.
Published: (2025)
by: Mullins, Mathieu, et al.
Published: (2025)
Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network
by: Li, Tianyu, et al.
Published: (2023)
by: Li, Tianyu, et al.
Published: (2023)
Turbulence teaches equivariance to neural networks
by: McConkey, Ryley, et al.
Published: (2026)
by: McConkey, Ryley, et al.
Published: (2026)
Physics-informed neural networks for hidden boundary detection and flow field reconstruction
by: Zhu, Yongzheng, et al.
Published: (2025)
by: Zhu, Yongzheng, et al.
Published: (2025)
An analysis and solution of ill-conditioning in physics-informed neural networks
by: Cao, Wenbo, et al.
Published: (2024)
by: Cao, Wenbo, et al.
Published: (2024)
Data-assisted, physics-informed propagators for recurrent flows
by: Lichtenegger, Thomas
Published: (2023)
by: Lichtenegger, Thomas
Published: (2023)
DNS and role of round-off error: Two-Dimensional Taylor-Green vortex problem
by: Suman, V. K., et al.
Published: (2025)
by: Suman, V. K., et al.
Published: (2025)
Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations
by: Xiang, Zixue, et al.
Published: (2021)
by: Xiang, Zixue, et al.
Published: (2021)
Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks
by: Matsuo, Mitsuaki, et al.
Published: (2021)
by: Matsuo, Mitsuaki, et al.
Published: (2021)
Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations
by: de Lara, Fernando Manrique, et al.
Published: (2022)
by: de Lara, Fernando Manrique, et al.
Published: (2022)
Learning thermoacoustic interactions in combustors using a physics-informed neural network
by: Mariappan, Sathesh, et al.
Published: (2023)
by: Mariappan, Sathesh, et al.
Published: (2023)
Active grid turbulence anomalies through the lens of physics informed neural networks
by: Angriman, Sofía, et al.
Published: (2024)
by: Angriman, Sofía, et al.
Published: (2024)
MSPINN: Multiple scale method integrated physics-informed neural networks for reconstructing transient natural convection
by: Ohashi, Nagahiro, et al.
Published: (2024)
by: Ohashi, Nagahiro, et al.
Published: (2024)
Quantifying the checkerboard problem to reduce numerical dissipation
by: Hopman, Johannes Arend, et al.
Published: (2024)
by: Hopman, Johannes Arend, et al.
Published: (2024)
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
by: Sedykh, Alexandr, et al.
Published: (2023)
by: Sedykh, Alexandr, et al.
Published: (2023)
ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
by: Thakur, Sukirt, et al.
Published: (2022)
by: Thakur, Sukirt, et al.
Published: (2022)
Inferring viscoplastic models from velocity fields: a physics-informed neural network approach
by: Lardy, Martin, et al.
Published: (2025)
by: Lardy, Martin, et al.
Published: (2025)
Mixed data-source transfer learning for a turbulence model augmented physics-informed neural network
by: Toma, Christian, et al.
Published: (2026)
by: Toma, Christian, et al.
Published: (2026)
A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation
by: Cao, Wenbo, et al.
Published: (2024)
by: Cao, Wenbo, et al.
Published: (2024)
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
by: Dreisbach, Maximilian, et al.
Published: (2024)
by: Dreisbach, Maximilian, et al.
Published: (2024)
Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh-Bénard convection
by: Mommert, Michael, et al.
Published: (2024)
by: Mommert, Michael, et al.
Published: (2024)
Multi-stream physics hybrid networks for solving Navier-Stokes equations
by: Sedykh, Aleksandr, et al.
Published: (2025)
by: Sedykh, Aleksandr, et al.
Published: (2025)
Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
by: Zou, Xintong, et al.
Published: (2025)
by: Zou, Xintong, et al.
Published: (2025)
Heat transfer in a planer diverging channel with a slot jet inlet
by: Rabby, Md Insiat Islam, et al.
Published: (2024)
by: Rabby, Md Insiat Islam, et al.
Published: (2024)
Equation identification for fluid flows via physics-informed neural networks
by: New, Alexander, et al.
Published: (2024)
by: New, Alexander, et al.
Published: (2024)
Physics-informed neural networks for multi-field visualization with single-color laser induced fluorescence
by: Ohashi, Nagahiro, et al.
Published: (2024)
by: Ohashi, Nagahiro, et al.
Published: (2024)
Informative and non-informative decomposition of turbulent flow fields
by: Arranz, Gonzalo, et al.
Published: (2024)
by: Arranz, Gonzalo, et al.
Published: (2024)
Solving nonlinear subsonic compressible flow in infinite domain via multi-stage neural networks
by: Qian, Xuehui, et al.
Published: (2026)
by: Qian, Xuehui, et al.
Published: (2026)
Tail observability and fourth-order closure recovery in physics-informed neural networks for Bhatnagar-Gross-Krook normal shocks
by: Roohi, Ehsan
Published: (2026)
by: Roohi, Ehsan
Published: (2026)
Inference of water waves surface elevation from horizontal velocity components using physics informed neural networks (PINN)
by: Sallam, Omar, et al.
Published: (2024)
by: Sallam, Omar, et al.
Published: (2024)
A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks
by: Cao, Wenbo, et al.
Published: (2024)
by: Cao, Wenbo, et al.
Published: (2024)
Digital twin of a large-aspect-ratio Rayleigh-Bénard experiment: Role of thermal boundary conditions, measurement errors and uncertainties
by: Vieweg, Philipp Patrick, et al.
Published: (2024)
by: Vieweg, Philipp Patrick, et al.
Published: (2024)
Similar Items
-
Discontinuity-aware KAN-based physics-informed neural networks
by: Lei, Guoqiang, et al.
Published: (2025) -
Turbulence model augmented physics informed neural networks for mean flow reconstruction
by: Patel, Yusuf, et al.
Published: (2023) -
Learning of viscosity functions in rarefied gas flows with physics-informed neural networks
by: Tucny, Jean-Michel, et al.
Published: (2023) -
An unstructured adaptive mesh refinement for steady flows based on physics-informed neural networks
by: Zhu, Yongzheng, et al.
Published: (2024) -
Finite difference physics-informed neural networks enable improved solution accuracy of the Navier-Stokes equations
by: Roy, Nityananda, et al.
Published: (2024)