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.

应用机器学习技术，本研究提出了预测飞机机身动态迫降载荷的方法。通过使用卷积自编码器（CAE）对空间载荷进行重构，并结合长短期记忆（LSTM）网络或基于库普曼算子的方法来预测载荷的瞬态演化。研究结果表明，LSTM 与深度解码器 CAE 的组合显示出最佳性能。