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 …
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 …
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 …
[HTML][HTML] A reinforcement learning strategy for p-adaptation in high order solvers
Reinforcement learning (RL) has emerged as a promising approach to automating decision
processes. This paper explores the application of RL techniques to optimise the polynomial …
processes. This paper explores the application of RL techniques to optimise the polynomial …
[HTML][HTML] Enhancing data representation in forging processes: Investigating discretization and R-adaptivity strategies with Proper Orthogonal Decomposition reduction
Effective data reduction techniques are crucial for enhancing computational efficiency in
complex industrial processes such as forging. In this study, we investigate various …
complex industrial processes such as forging. In this study, we investigate various …
Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards
Simulating physical systems is essential in engineering, but analytical solutions are limited
to straightforward problems. Consequently, numerical methods like the Finite Element …
to straightforward problems. Consequently, numerical methods like the Finite Element …
[PDF][PDF] Results in Engineering
Reinforcement learning (RL) has emerged as a promising approach to automating decision
processes. This paper explores the application of RL techniques to optimise the polynomial …
processes. This paper explores the application of RL techniques to optimise the polynomial …
DynAMO: Dynamic Anticipatory Mesh Optimization for Hyperbolic PDEs using Reinforcement Learning
ABSTRACT Adaptive Mesh Refinement (AMR) is a popular technique for improving
efficiency of finite element simulations by selectively modifying resolution in different regions …
efficiency of finite element simulations by selectively modifying resolution in different regions …