人类主导、物理约束的多智能体工作流，2.9 小时构建可审计的土塞演化模型
Human-guided physics-constrained AI agents construct an auditable model of soil-plug evolution
工程师定规矩，AI 智能体 2.9 小时写出 6 套土塞演化求解器，隆起误差从 58.4% 降到 9.0%。
一篇 arXiv 论文提出人在环路、物理约束的多智能体工作流：人类专家划定可用物理假设与建模边界，智能体负责检索证据、推导方程、实现求解器并审计从理论到代码的全链条。在吸力沉箱安装的土塞演化问题上，工作流在 2.9 小时智能体执行时间内生成并审计了 6 套公式体系。其中在几何基线上加入渗流驱动的孔隙比演化后，14 个剖面的最终隆起平均绝对误差从 58.4% 降至 9.0%；最终选定的模型再加入近壁剪胀，在 9 个最终状态案例上的平均绝对百分比误差为 12.4%，在 5 条过程历史的端点处为 4.2%。独立审计在 36 项预定义检查全部通过后仍发现 5 处实现问题，盲测重放则复现了全部 9 个目标问题。
Human-guided physics-constrained AI agents construct an auditable model of soil-plug evolution
Engineering predictions require physical mechanisms to be translated consistently into equations, discretization, code, and validation, yet errors can propagate despite local checks. Artificial-intelligence (AI) agents automate scientific tasks, but coordinating and independently auditing the theory-to-solver process under physical constraints and human oversight remains unresolved. We introduce a human-in-the-loop, physics-constrained multi-agent workflow where human experts define admissible physics and modeling boundaries, while agents retrieve evidence, derive equations, implement solvers, and audit the theory-to-code chain. Applied to soil-plug evolution during suction-caisson installation, the workflow generated and audited 6 formulations in 2.9 h of agent execution once physical knowledge and inputs were prepared. Among these formulations, adding seepage-driven soil void-ratio evolution to the geometric baseline reduced mean absolute final-heave error from 58.4% to 9.0% across 14 profiles; the selected model further incorporated near-wall dilation and achieved mean absolute percentage errors of 12.4% across 9 final-state cases and 4.2% at the endpoints of 5 process histories. Beyond predictive performance, blinded replay recovered all 9 target problems, while an independent audit uncovered 5 implementation problems after 36 predefined checks had passed. Overall, this work extends multi-agent AI beyond task automation toward human-governed engineering solvers.
