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 for
The increasing sophistication of reinforcement learning and multi-agent systems is enabling breakthroughs in complex computational engineering problems previously reliant on manual input.
Automating high-quality mesh generation, a fundamental bottleneck in computational engineering, could significantly accelerate design, simulation, and manufacturing across various industries.
Previously manual or semi-manual mesh generation workflows, which are critical for accurate simulations, can now be fully automated by AI, reducing human effort and improving efficiency.
- · AI/ML researchers
- · Computational engineers
- · Manufacturing sector
- · Design and simulation software providers
- · Manual meshing specialists
Faster and more accurate computational engineering simulations become more accessible.
Accelerated product development cycles and reduced time-to-market for complex designs across aerospace, automotive, and other industries.
The widespread adoption of automated meshing could lower barriers to entry for advanced engineering, leading to new innovations and competitive landscapes.
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Read at arXiv cs.LG