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.

翻译：物理信息机器学习通常通过曲线误差进行评估，尽管工程应用依赖于下游决策：候选排序、避免不可行设计以及限制遗憾。我们提出 pinn-gym，一个用于材料条件晶格设计的开放基准测试，该基准将透明的降阶压碎-冲击 oracle 与五张可打印聚合物卡片、无量纲力响应目标以及一套涵盖曲线保真度、物理可允许性、top-k 检索和质量遗憾的协议相结合。在跨材料、混合和跨材料设置中，低 nRMSE 常常不足以识别有用的设计选择。物理信息损失会改变权衡，而非单调地改善所有指标，而无量纲条件化能够提高可比性，而不会使迁移变得对称。该基准并非经过认证的材料模型；在已发布的 oracle、候选生成器和材料卡片中，pinn-gym 提供了一个可重复的测试平台，用于评估 PIML 代理作为决策系统，而非仅曲线预测器。