Mesh optimization using an improved self-organizing mechanism
As more powerful computing hardware enables higher resolution simulations, a fast and
flexible mesh optimization method is becoming increasingly indispensable for …
flexible mesh optimization method is becoming increasingly indispensable for …
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 …
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 …
Multi-agent reinforcement learning for adaptive mesh refinement
Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of
complex physical phenomenon, as it allocates limited computational budget based on the …
complex physical phenomenon, as it allocates limited computational budget based on the …
DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws
We introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh
Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost …
Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost …
Quasi-optimal hp-finite element refinements towards singularities via deep neural network prediction
We show how to construct a deep neural network (DNN) expert to predict quasi-optimal hp-
refinements for a given finite element problem in presence of singularities. The main idea is …
refinements for a given finite element problem in presence of singularities. The main idea is …
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 …
GMR-Net: GCN-based mesh refinement framework for elliptic PDE problems
M Kim, J Lee, J Kim - Engineering with Computers, 2023 - Springer
In this study, we propose a new approach for automatically generating high-quality non-
uniform meshes based on supervised learning. The proposed framework, GMR-Net, is …
uniform meshes based on supervised learning. The proposed framework, GMR-Net, is …
Spectral-Refiner: Fine-Tuning of Accurate Spatiotemporal Neural Operator for Turbulent Flows
Recent advancements in operator-type neural networks have shown promising results in
approximating the solutions of spatiotemporal Partial Differential Equations (PDEs) …
approximating the solutions of spatiotemporal Partial Differential Equations (PDEs) …
Multilevel CNNs for parametric pdes based on adaptive finite elements
JE Schütte, M Eigel - arXiv preprint arXiv:2408.10838, 2024 - arxiv.org
A neural network architecture is presented that exploits the multilevel properties of high-
dimensional parameter-dependent partial differential equations, enabling an efficient …
dimensional parameter-dependent partial differential equations, enabling an efficient …