Differentiable hybrid neural modeling for fluid-structure interaction
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Publication:6198155
DOI10.1016/j.jcp.2023.112584arXiv2303.12971OpenAlexW4387849567MaRDI QIDQ6198155
Publication date: 21 February 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2303.12971
fluid-structure interactionsdeep neural networkdifferentiable programmingscientific machine learningcomputational fluid dynamics acceleration
Basic methods in fluid mechanics (76Mxx) Incompressible viscous fluids (76Dxx) Coupling of solid mechanics with other effects (74Fxx)
Cites Work
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- Fluid-structure interaction involving large deformations: 3D simulations and applications to biological systems
- A simple and efficient direct forcing immersed boundary framework for fluid-structure interactions
- Combined immersed-boundary finite-difference methods for three-dimensional complex flow simulations
- An arbitrary Lagrangian-Eulerian finite element method for transient dynamic fluid-structure interactions
- Data-driven reduced order modeling for time-dependent problems
- A finite element/neural network framework for modeling suspensions of non-spherical particles. Concepts and medical applications
- A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- An immersed boundary fluid-structure interaction method for thin, highly compliant shell structures
- Deep neural network modeling of unknown partial differential equations in nodal space
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Prediction of aerodynamic flow fields using convolutional neural networks
- An immersed boundary method with direct forcing for the simulation of particulate flows
- Flow patterns around heart valves: A numerical method
- Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing
- Immersed Methods for Fluid–Structure Interaction
- The immersed boundary method
- Arbitrary Lagrangian-Eulerianc finite element method for unsteady, convective, incompressible viscous free surface fluid flow
- Formulation of Rayleigh damping and its extensions
- Subgrid modelling for two-dimensional turbulence using neural networks
- Data-Driven Discovery of Closure Models
- Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- Flapping dynamics of a flag in a uniform stream
- Numerical Solution of the Navier-Stokes Equations
- JAX-fluids: a fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
