发表机构
ETH Zürich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种生成式世界模型，用于预测激光熔池动力学，并通过可微动力学优化激光调度，实现熔池深度控制，为制造过程提供实时决策支持。
AI 中文摘要
世界模型通过学习环境如何响应动作，正成为一种通过想象未来进行规划的强大范式，正在改变游戏、机器人和自动驾驶领域的决策制定。将这一能力引入制造业，可以在高保真模拟无法企及的时间尺度上实现工艺决策。在此，我们引入一种针对局部高动态激光熔池的生成式世界模型，该模型根据候选动作下的温度和相形态历史预测熔池演化。其生成式潜在动力学捕捉了未解析熔融流动的影响，使得在瞬态激光输入下，比确定性回归器能够进行更准确的递归展开。由于学习到的动力学是可微的，该模型可直接作为预测控制对象。通过想象未来的梯度优化激光调度，以在未见过的几何形状、路径和初始化条件下调节熔池深度。我们进一步将此优化蒸馏为一个摊销策略，该策略通过单次前向传播产生控制动作，为在机器上的实际部署提供了概念验证。
英文摘要
World models, which learn how environments respond to actions, are emerging as a powerful paradigm for planning through imagined futures, transforming decision-making across games, robotics and autonomous driving. Bringing this capability to manufacturing could enable process decisions on timescales inaccessible to high-fidelity simulation. Here we introduce a generative world model for localized highly dynamic laser melt pool that predicts evolution from histories of temperature and phase morphology under candidate actions. Its generative latent dynamics capture the effects of unresolved melt flow, enabling more accurate recursive rollouts than deterministic regressors under transient laser inputs. Because the learned dynamics are differentiable, the model can serve directly as a predictive control plant. Gradients through imagined futures optimize laser schedules that regulate melt-pool depth over previously unseen geometry, path, initialization. We further distil this optimization into an amortized policy that produces control actions in a single forward pass, providing a proof of concept for real deployment on machines.
