We present approaches to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstruction of the spatial loads using a convolutional autoencoder (CAE) and the transient evolution of these loads in a subsequent part. Different CAE strategies are assessed and combined with either long short-term memory (LSTM) networks or Koopman-operator based methods to predict the transient behaviour. The training data is compiled by an extension of the momentum method of von-Karman and Wagner and the rationale of the training approach is briefly summarised. The application included refers to a full-scale fuselage of a DLR-D150 aircraft for a range of horizontal and vertical approach velocities at 6{\deg} incidence. Results indicate a satisfactory level of predictive agreement for all four investigated surrogate models examined, with the combination of an LSTM and a deep decoder CAE showing the best performance.

翻译：我们提出了利用机器学习预测飞机机身动态着水载荷的方法。采用的学习过程分为两部分：首先使用卷积自编码器（CAE）重建空间载荷分布，随后在后续部分中预测这些载荷的瞬态演化。我们评估了不同的CAE策略，并将其与长短期记忆（LSTM）网络或基于Koopman算子的方法相结合，以预测瞬态行为。训练数据通过扩展冯·卡门和瓦格纳的动量方法编制而成，并简要总结了训练方法的基本原理。应用实例涉及DLR-D150飞机全尺寸机身在一系列水平与垂直接近速度（6°攻角）下的工况。结果表明，所有四种替代模型均达到令人满意的预测一致性水平，其中LSTM与深度解码器CAE的组合显示出最佳性能。