arXiv:2605.20190v1 Announce Type: new Abstract: Iterative industrial design-simulation optimization is bottlenecked by the CAD-CAE semantic gap: translating simulation feedback into valid geometric edits under diverse, coupled constraints. To fill this gap, we propose COSMO-Agent (Closed-loop Optimization, Simulation, and Modeling Orchestration), a tool-augmented reinforcement learning (RL) framework that teaches LLMs to complete the closed-loop CAD-CAE process. Specifically, we cast CAD generation, CAE solving, result parsing, and geometry revision as an interactive RL environment, where an L
The increasing sophistication of LLMs and reinforcement learning allows for more complex, multi-step automation in traditionally human-intensive design processes.
This development represents a significant step towards fully autonomous engineering design, potentially collapsing current white-collar workflows in CAD/CAE and accelerating product development cycles.
The bottleneck of translating simulation feedback into valid geometric edits in industrial design-simulation optimization is being addressed by AI, integrating previously disparate software tasks.
- · AI software developers
- · Manufacturing industries
- · Engineering firms adopting AI tools
- · Traditional CAD/CAE software vendors (if slow to adapt)
- · Entry-level CAD/CAE designers
- · Consulting firms specializing in design optimization
Automated design cycles lead to faster product innovation and reduced time to market for complex engineered goods.
The demand for specialized engineering talent may shift from iterative design execution to AI model supervision and validation.
This could lead to a ' Cambrian explosion' of novel product designs and materials, previously too complex or time-consuming to explore manually.
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Read at arXiv cs.AI