Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence
From MaRDI portal
Publication:6663283
DOI10.1016/j.cma.2024.117478MaRDI QIDQ6663283
Deepak Akhare, Xiantao Fan, Jian-Xun Wang
Publication date: 14 January 2025
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
super-resolutiondeep neural networkdifferentiable programmingscientific machine learninggenerative AIconditional diffusion model
Cites Work
- Title not available (Why is that?)
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- When and why PINNs fail to train: a neural tangent kernel perspective
- Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- A bi-fidelity ensemble Kalman method for PDE-constrained inverse problems in computational mechanics
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing
- A physics-informed diffusion model for high-fidelity flow field reconstruction
- Subgrid modelling for two-dimensional turbulence using neural networks
- Some Recent Developments in Turbulence Closure Modeling
- Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons
- Ensemble Kalman method for learning turbulence models from indirect observation data
- Learning data-driven discretizations for partial differential equations
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Homogeneous, Isotropic Turbulence
- Invariant recurrent solutions embedded in a turbulent two-dimensional Kolmogorov flow
- SeismicNET: physics-informed neural networks for seismic wave modeling in semi-infinite domain
- Differentiable hybrid neural modeling for fluid-structure interaction
- Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
This page was built for publication: Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6663283)