# Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification
###### Abstract
In modern DL, the focus is on constructing more efficient and streamlined architectures, wherein key components are accentuated, and essential attributes from the original data are preserved. This process of crafting architectures involves the meticulous extraction of feature representations from data, which is commonly referred to as feature engineering. However, this practice demands specialised knowledge and a significant investment of time. Moreover, the intricacies of feature engineering differ across various data types, thus posing a challenge in establishing universally applicable procedures. An established option for feature extraction, alleviating feature engineering, is delivered in the form of an autoencoder, a type of neural network that possesses the capability to autonomously learn features from unlabeled data using unsupervised learning techniques. This unique ability obviates the need for extensive feature engineering [22; 23; 24]. Consisting of two integral components: an autoencoder comprises an encoder responsible for mapping inputs to a desired latent space and a decoder that skillfully reverts the latent space back to the original input domain. By harnessing appropriately curated training data, autoencoders have the capacity to generate a latent representation, thereby negating the necessity for labour-intensive feature engineering endeavours. However, general autoencoders may not capture spatial features, such as images, or sequential information when dealing with dynamics, like time series forecasting. To address the limitation of capturing spatial features, the use of a CNN-based autoencoder is recommended, whereas, an RNN-based autoencoder is usually employed for learning features from time series data. Deep CNN-based autoencoders (DCAEs) excel at extracting spatial features from image-based input data, they may not be sufficient for extracting features from sequences of images, particularly in cases involving full wavefield data, which contain numerous sequential frames/images for each delamination scenario. For such situations, ConvLSTM [25] is employed. ConvLSTM combines CNN and LSTM, thus enabling it to effectively learn features from sequences of images. In ConvLSTM architecture, the CNN is responsible for learning features from images, while LSTM retains sequential information. DCAE-based surrogate modelling has been implemented in [26; 27]. [PERSON] et al. [26] developed a DCAE framework for the purpose of extracting latent features from spatial properties and investigating adaptive surrogate estimation to sequester CO\({}_{2}\) into heterogeneous deep saline aquifers. They used a DCAE and a fully convolutional network for reducing the computational costs and extracting dimensionality-reduced features for conserving spatial characteristics. [PERSON] et al. [27] presented a nonintrusive DL-based surrogate modelling scheme for the predictive modelling of complex systems, which they described using parametrized time-dependent partial differential equations. Recently, [PERSON] et al. [28] proposed an encoding convolution long short-term memory (encoding ConvLSTM) framework for building a surrogate structural model with spatiotemporal evolution, thus estimating structural spatiotemporal states and predicting dynamic responses under future dynamic load conditions. [PERSON] and [PERSON] [29] presented a hybrid CNN-recurrent neural network (RNN) for handling spatiotemporal information extraction challenges in impact damage detection problems. Inspired by the successful implementations of CNN and RNN for the representation of spatiotemporal dynamics, we explore for the first time the potential of deep neural networks for the modelling of complex behaviour of guided waves interacting with defects. Therefore, in this work, the damage identification method that relies on the anomalies of propagating guided waves is employed. It is an inverse method in opposition to the most commonly used data-driven methods.