Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Xing, Fulin, Li, Junjie, Tao, Ze, Liu, Fujun, Tan, Yong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
by: Zhou, Baitong, et al.
Published: (2026)
by: Zhou, Baitong, et al.
Published: (2026)
Learnable Viscosity Modulation in Physics-Informed Neural Networks for Incompressible Flow Reconstruction
by: Xu, Ke, et al.
Published: (2026)
by: Xu, Ke, et al.
Published: (2026)
LSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow
by: Tao, Ze, et al.
Published: (2025)
by: Tao, Ze, et al.
Published: (2025)
Physics-Informed Neural Networks for microflows: Rarefied Gas Dynamics in Cylinder Arrays
by: Tucny, Jean-Michel, et al.
Published: (2025)
by: Tucny, Jean-Michel, et al.
Published: (2025)
Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks
by: Roohi, Ehsan, et al.
Published: (2025)
by: Roohi, Ehsan, et al.
Published: (2025)
LSTM-PINN for Steady-State Electrothermal Transport: Preserving Multi-Field Consis tency in Strongly Coupled Heat and Fluid Flow
by: Zhou, Yuqing, et al.
Published: (2026)
by: Zhou, Yuqing, et al.
Published: (2026)
Using Physics Informed Neural Network (PINN) and Neural Network (NN) to Improve a $k-ω$ Turbulence Model
by: Davidson, Lars
Published: (2025)
by: Davidson, Lars
Published: (2025)
Discontinuity Computing using Physics-Informed Neural Network
by: Liu, Li, et al.
Published: (2022)
by: Liu, Li, et al.
Published: (2022)
Marangoni Interfacial Instability Induced by Solute Transfer Across Liquid-Liquid Interfaces
by: Li, Xiangwei, et al.
Published: (2024)
by: Li, Xiangwei, et al.
Published: (2024)
Physics-Informed Neural Networks for Weakly Compressible Flows Using Galerkin-Boltzmann Formulation
by: Aygun, Atakan, et al.
Published: (2024)
by: Aygun, Atakan, et al.
Published: (2024)
Physics-Informed Deep Operator Learning for Computational Hydraulics Modeling
by: Liu, Xiaofeng, et al.
Published: (2026)
by: Liu, Xiaofeng, et al.
Published: (2026)
Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
by: Chou, Yi En, et al.
Published: (2025)
by: Chou, Yi En, et al.
Published: (2025)
ELPINN: Eulerian Lagrangian Physics-Informed Neural Network
by: Thakur, Sukirt, et al.
Published: (2025)
by: Thakur, Sukirt, et al.
Published: (2025)
Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations
by: Li, Yue, et al.
Published: (2025)
by: Li, Yue, et al.
Published: (2025)
Physics-Informed Neural Networks for Transonic Flows around an Airfoil
by: Wassing, Simon, et al.
Published: (2024)
by: Wassing, Simon, et al.
Published: (2024)
A Liquid-Fueled Reactor Network Model for Enhanced NOx Prediction in Gas Turbine Combustors
by: John, Philip, et al.
Published: (2025)
by: John, Philip, et al.
Published: (2025)
Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows
by: Delcey, Mickaël, et al.
Published: (2024)
by: Delcey, Mickaël, et al.
Published: (2024)
Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks
by: Warey, Alok, et al.
Published: (2021)
by: Warey, Alok, et al.
Published: (2021)
Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows
by: Su, Zijie, et al.
Published: (2024)
by: Su, Zijie, et al.
Published: (2024)
Physics-Informed Neural Networks for the Korteweg-de Vries Equation for Internal Solitary Wave Problem: Forward Simulation and Inverse Parameter Estimation
by: Kang, Ming, et al.
Published: (2025)
by: Kang, Ming, et al.
Published: (2025)
FlexPINN: Modeling Fluid Dynamics and Mass Transfer in 3D Micromixer Geometries Using a Flexible Physics-Informed Neural Network
by: Hassanzadeh, Meraj, et al.
Published: (2025)
by: Hassanzadeh, Meraj, et al.
Published: (2025)
An approximate Riemann solver approach in Physics-Informed Neural Networks for hyperbolic conservation laws
by: Urbán, Jorge F., et al.
Published: (2025)
by: Urbán, Jorge F., et al.
Published: (2025)
Layering Theory of Liquids at Solid Interfaces: Interfacial Layering Oscillator Model
by: Sun, Chengzhen, et al.
Published: (2025)
by: Sun, Chengzhen, et al.
Published: (2025)
Modelling of Counter-Current Gas-Liquid Annular Flow in a Vertical Annulus
by: Firouzi, Mahshid
Published: (2025)
by: Firouzi, Mahshid
Published: (2025)
Deep Learning Surrogates for Gas Dynamics: A Physics-Informed Pedagogical Approach
by: Roohi, Ehsan
Published: (2025)
by: Roohi, Ehsan
Published: (2025)
Sparse-Supervised Hybrid Parameterized Physics-Informed Neural Networks for Incompressible Flows Across Reynolds Numbers
by: Jangir, A., et al.
Published: (2026)
by: Jangir, A., et al.
Published: (2026)
A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
by: Barman, Biswanath, et al.
Published: (2026)
by: Barman, Biswanath, et al.
Published: (2026)
Physics Informed Neural Networks for Free Shear Flows
by: Raghu, Siddharth, et al.
Published: (2024)
by: Raghu, Siddharth, et al.
Published: (2024)
Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
by: Dong, Guodan, et al.
Published: (2026)
by: Dong, Guodan, et al.
Published: (2026)
Liquid Sheet Breakup in Gas-Centered Swirl Coaxial Atomizers
by: Kulkarni, V., et al.
Published: (2024)
by: Kulkarni, V., et al.
Published: (2024)
Numerical Solution to the Riemann Problem for a Liquid-Gas Two-phase Isentropic Flow Model
by: Rab, Abdul
Published: (2025)
by: Rab, Abdul
Published: (2025)
Physics-Informed Neural Networks for Multi-Phase Flow in Porous Media Considering Dual Shocks and Interphase Solubility
by: Zhang, Jingjing, et al.
Published: (2024)
by: Zhang, Jingjing, et al.
Published: (2024)
An Efficient Wavelet-based Physics Informed Residual Neural Networks for Flow Field Reconstruction with Extremely Sparse Data
by: Barman, Biswanath, et al.
Published: (2026)
by: Barman, Biswanath, et al.
Published: (2026)
Unsteady flow predictions around an obstacle using Geometry-Parameterized Dual-Encoder Physics-Informed Neural Network
by: Wang, Zekun, et al.
Published: (2026)
by: Wang, Zekun, et al.
Published: (2026)
A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network
by: Zhang, Han, et al.
Published: (2023)
by: Zhang, Han, et al.
Published: (2023)
Hard Constraint Projection in a Physics Informed Neural Network
by: Horne, Miranda J. S., et al.
Published: (2026)
by: Horne, Miranda J. S., et al.
Published: (2026)
Physics-Informed Neural Networks for Parametric Compressible Euler Equations
by: Wassing, Simon, et al.
Published: (2023)
by: Wassing, Simon, et al.
Published: (2023)
Physics-Informed Neural Networks for Solving the Two-Dimensional Shallow Water Equations with Terrain Topography and Rainfall Source Terms
by: Tian, Yongfu, et al.
Published: (2025)
by: Tian, Yongfu, et al.
Published: (2025)
Correlations for the Interphase Drag in the Two-Fluid Model of Gas--Liquid Flows through Packed-Bed Reactors
by: Nagrani, Pranay P., et al.
Published: (2024)
by: Nagrani, Pranay P., et al.
Published: (2024)
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
by: Xu, Weiming, et al.
Published: (2023)
by: Xu, Weiming, et al.
Published: (2023)
Similar Items
-
High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
by: Zhou, Baitong, et al.
Published: (2026) -
Learnable Viscosity Modulation in Physics-Informed Neural Networks for Incompressible Flow Reconstruction
by: Xu, Ke, et al.
Published: (2026) -
LSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow
by: Tao, Ze, et al.
Published: (2025) -
Physics-Informed Neural Networks for microflows: Rarefied Gas Dynamics in Cylinder Arrays
by: Tucny, Jean-Michel, et al.
Published: (2025) -
Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks
by: Roohi, Ehsan, et al.
Published: (2025)