Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Topology Optimization in Engineering
Abstract
This paper introduces the Dynamic Topology Algorithm Reinforcement Learning (DTARL), a novel reinforcement learning framework designed to optimize complex systems by dynamically adjusting topology. Traditional topology optimization methods often rely on manual parameter tuning, while DTARL leverages reinforcement learning to guide the optimization process, resulting in improved system performance. The core mechanism involves a carefully designed reward and penalty system, which iteratively refines the topology based on the learned dynamics. This approach offers a more intelligent and adaptable solution compared to conventional methods, demonstrating significant improvements in system performance across various complex scenarios. The paper details the algorithm's architecture, explores its efficacy through simulations, and provides a comprehensive analysis of its performance.
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