analiza aiqbench
Dmsh introduces a novel multi-agent reinforcement learning framework that fully automates all-quadrilateral mesh generation, effectively eliminating the need for heuristic tuning and semi-manual workflows common in computational engineering. By formulating the meshing process as a Markov Decision Process solved with a parametric Soft Actor-Critic architecture, the system achieves superior automation, robustness, and mesh quality across complex geometries. This approach establishes a new paradigm for learning-based mesh generation, offering significant potential to streamline fundamental bottlenecks in numerical analysis and simulation.
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
arXiv:2606.10601v1 Announce Type: cross Abstract: Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. In this paper, we introduce Dmsh, a first fully automated reinforcement learning pipeline that unifies geometric decomposition and quadrilateral mesh generation within a single learning-based framework. Dmsh decomposes the problem through three coordinated agents handling topology simplification, geometric regularization, and mesh generation. The meshing process is formulated as a Markov Decision Process and solved using a parametric Soft Actor-Critic architecture with decoupled critics, enabling efficient exploration of a hybrid discrete-continuous action space. A curriculum learning strategy ensures scalability from simple domains to highly complex geometries, suppressing seed variance. By design, the recursive decomposition enables parallel meshing of subregions, yielding globally conforming all-quadrilateral meshes without post hoc correction. Across a wide range of benchmarks, Dmsh consistently outperforms existing methods in automation, robustness, and mesh quality, establishing a new paradigm for learning-based mesh generation.
Abstract:Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. In this paper, we introduce Dmsh, a first fully automated reinforcement learning pipeline that unifies geometric decomposition and quadrilateral mesh generation within a single learning-based framework. Dmsh decomposes the problem through three coordinated agents handling topology simplification, geometric regularization, and mesh generation. The meshing process is formulated as a Markov Decision Process and solved using a parametric Soft Actor-Critic architecture with decoupled critics, enabling efficient exploration of a hybrid discrete-continuous action space. A curriculum learning strategy ensures scalability from simple domains to highly complex geometries, suppressing seed variance. By design, the recursive decomposition enables parallel meshing of subregions, yielding globally conforming all-quadrilateral meshes without post hoc correction. Across a wide range of benchmarks, Dmsh consistently outperforms existing methods in automation, robustness, and mesh quality, establishing a new paradigm for learning-based mesh generation.
Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2606.10601 [math.NA]
(or arXiv:2606.10601v1 [math.NA] for this version)
https://doi.org/10.48550/arXiv.2606.10601 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 252 cuvinte
Subiecte conexe
MultiAgent-OPD: End-to-End Training of Hierarchical Multi-Agent Systems with On-Policy Distillation
arXiv · 19 oct. 2026
Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures
arXiv · 22 iul. 2026
MOIRA: Policy Memory and Belief Revision for Language-Model Agents in Non-Stationary Pair Trading
arXiv · 21 oct. 2026
Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents
arXiv · 23 iul. 2026