用决策效果评估物理信息机器学习模型，更贴近工程实际。
Decision-Aware Evaluation of Physics-Informed Surrogates
- 构建pinn-gym基准，融合物理可接受性与决策指标
- 低误差不等于好设计，需综合考虑排名与损失风险
- 适合关注工程决策的模型开发者和材料设计研究者
物理信息机器学习常以曲线误差评估，但工程应用依赖下游决策：候选排序、避免不可行设计、控制后悔值。本文提出pinn-gym，一个面向材料条件晶格设计的开源基准，结合透明的降阶冲击-压溃代理模型、五种可打印聚合物材料卡、无量纲力-响应目标及涵盖曲线保真度、物理可行性、Top-k检索和质量后悔值的评估协议。在单材料、合并及跨材料设置中，低nRMSE常不足以选出有效设计。物理信息损失会改变权衡关系，而非单调提升所有指标；无量纲条件提升可比性，但不保证迁移对称性。该基准非认证材料模型，但在已发布代理模型、候选生成器与材料卡范围内，提供可复现的PIML代理模型作为决策系统评估平台。
原文摘要 · Abstract (English)
Physics-informed machine learning is often assessed by curve error, although engineering use depends on downstream decisions: ranking candidates, avoiding infeasible designs and limiting regret. We introduce pinn-gym, an open benchmark for material-conditioned lattice design that couples a transparent reduced-order crush-and-impact oracle with five printable polymer cards, dimensionless force-response targets and a protocol spanning curve fidelity, physical admissibility, top-k retrieval and mass regret. Across per-material, pooled and cross-material settings, low nRMSE is frequently insufficient to identify useful design selections. Physics-informed losses alter trade-offs rather than monotonically improving all metrics, and dimensionless conditioning improves comparability without making transfer symmetric. The benchmark is not a certified material model; within the released oracle, candidate generator and material cards, pinn-gym provides a reproducible testbed for evaluating PIML surrogates as decision systems rather than curve predictors alone.
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