A review and comparison of error estimators for anisotropic mesh adaptation for flow simulations
Automated mesh adaptation is known to be an efficient way to control discretization errors in
Computational Fluid Dynamics (CFD) simulations. It offers the added advantage that the …
Computational Fluid Dynamics (CFD) simulations. It offers the added advantage that the …
Swarm reinforcement learning for adaptive mesh refinement
Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …
M2N: Mesh movement networks for PDE solvers
Abstract Numerical Partial Differential Equation (PDE) solvers often require discretizing the
physical domain by using a mesh. Mesh movement methods provide the capability to …
physical domain by using a mesh. Mesh movement methods provide the capability to …
[HTML][HTML] Neural control of discrete weak formulations: Galerkin, least squares & minimal-residual methods with quasi-optimal weights
There is tremendous potential in using neural networks to optimize numerical methods. In
this paper, we introduce and analyze a framework for the neural optimization of discrete …
this paper, we introduce and analyze a framework for the neural optimization of discrete …
Towards a new paradigm in intelligence-driven computational fluid dynamics simulations
Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical
phenomena and exploring the principles of fluid mechanics. However, CFD numerical …
phenomena and exploring the principles of fluid mechanics. However, CFD numerical …
Machine learning mesh-adaptation for laminar and turbulent flows: applications to high-order discontinuous Galerkin solvers
We present a machine learning-based mesh refinement technique for steady and unsteady
incompressible flows. The clustering technique proposed by Otmani et al.(Phys Fluids 35 …
incompressible flows. The clustering technique proposed by Otmani et al.(Phys Fluids 35 …
E2n: error estimation networks for goal-oriented mesh adaptation
Given a partial differential equation (PDE), goal-oriented error estimation allows us to
understand how errors in a diagnostic quantity of interest (QoI), or goal, occur and …
understand how errors in a diagnostic quantity of interest (QoI), or goal, occur and …
MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations
A Heyrani Nobari, J Rey… - Journal of …, 2024 - asmedigitalcollection.asme.org
In many design automation applications, accurate segmentation and classification of 3D
surfaces and extraction of geometric insight from 3D models can be pivotal. This paper …
surfaces and extraction of geometric insight from 3D models can be pivotal. This paper …
Conformal predictions enhanced expert-guided meshing with graph neural networks
AH Nobari, J Rey, S Kodali, M Jones… - arXiv preprint arXiv …, 2023 - arxiv.org
Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but
accurate simulations are dependent upon proper meshing of the simulation domain. While …
accurate simulations are dependent upon proper meshing of the simulation domain. While …
Unstructured mesh size control method based on artificial neural network
W Nianhua, L Peng, C Xinghua, Z Laiping… - Chinese Journal of …, 2021 - lxxb.cstam.org.cn
Automatic mesh generation and adaptation are bottleneck problems restricting
computational fluid dynamics (CFD). Grid quality, efficiency, flexibility, automation level, and …
computational fluid dynamics (CFD). Grid quality, efficiency, flexibility, automation level, and …