Deep learning methods -- physics-informed neural networks (PINNs), deep operator networks (DeepONet), and graph network simulators (GNS) -- are increasingly proposed for geotechnical problems. This paper tests these methods against traditional solvers on canonical problems: wave propagation and beam-foundation interaction. PINNs run 90,000 times slower than finite difference with larger errors. DeepONet requires thousands of training simulations and breaks even only after millions of evaluations. Multi-layer perceptrons fail catastrophically when extrapolating beyond training data -- the common case in geotechnical prediction. GNS shows promise for geometry-agnostic simulation but faces scaling limits and cannot capture path-dependent soil behavior. For inverse problems, automatic differentiation through traditional solvers recovers material parameters with sub-percent accuracy in seconds. We recommend: use automatic differentiation for inverse problems; apply site-based cross-validation to account for spatial autocorrelation; reserve neural networks for problems where traditional solvers are genuinely expensive and predictions remain within the training envelope. When a method is four orders of magnitude slower with less accuracy, it is not a viable replacement for proven solvers.

翻译：深度学习方法——物理信息神经网络（PINNs）、深度算子网络（DeepONet）和图网络模拟器（GNS）——正越来越多地被提出用于解决岩土工程问题。本文在典型问题（波传播与梁-基础相互作用）上测试了这些方法与传统求解器的性能。PINNs的运行速度比有限差分法慢90,000倍，且误差更大。DeepONet需要数千次训练模拟，仅在数百万次评估后才能达到盈亏平衡点。多层感知器在训练数据外推时（岩土预测中的常见情况）会出现灾难性失败。GNS在几何无关模拟方面显示出潜力，但面临扩展性限制，且无法捕捉路径依赖的土体行为。对于反问题，通过传统求解器进行自动微分可在数秒内以低于百分之一的精度恢复材料参数。我们建议：对反问题使用自动微分；应用基于场地的交叉验证以考虑空间自相关性；仅当传统求解器确实昂贵且预测保持在训练范围内时，才保留神经网络方法。当一种方法速度慢四个数量级且精度更低时，它并不能成为成熟求解器的可行替代方案。