## 1 Introduction  
Composite materials are used in several applications such as aircraft structural components or in civil infrastructures due to their high structural performance. These materials may exhibit damage mechanisms such as matrix cracking, fiber break, fiber/matrix debonding and delamination. Real-time monitoring of composite damage is thus of primary importance to prevent failure. Prognostics and health management (PHM) of components involve both diagnostic and prognostic steps [1, 2]. Diagnostics consist in detecting and identifying the different damage mechanisms. This step is crucial for the reliability of these structures [3, 4] and ensuring successful prognostic strategies, which still need to be developed [5, 6, 7]. These methods exploit the data measured by a network of sensors located on the structure in order to determine the damage state; then, the prognostic strategies can predict the remaining useful lifetime (RUL) of the structure [8, 9]. This procedure involves several steps: damage (1) detection, (2) localization, (3) classification and quantification and, finally, (4) prognostics. Therefore, damage detection and identification are crucial steps to perform prognostics and RUL prediction. An acceptable prognostic method starts with robust diagnostics, since any future predictions are affected by the uncertainties in the estimated damage state or wrong identification of damage mechanisms. Real-time monitoring of composite damage may be carried out using Acoustic Emission (AE), whose principle is described hereafter. Damage occurring in composite materials results in the nucleation and propagation of cracks or debonding and thus energy release. A part of this energy is released as elastic waves that propagate through the material. These waves can be detected by sensors that are placed on the surface of the specimen. Damage mechanisms thus act as acoustic emission sources. A main advantage of AE is the possibility to detect damage in situ even at an early stage in a nondestructive manner. However, the main difficulty consists in establishing a robust link between the different damage mechanisms and the signals acquired by the sensors. In common practice, most of the AE analysis is based on an empirical approach which consists of gathering AE signals with similar characteristics into groups and assuming that each group corresponds to a given damage mechanism. These correlations between the sources and the AE signals generally lack validity. Indeed, few AE signal labels are confirmed by direct observations of the sources due to the difficulty of experimentally controlling the sources. Nevertheless, the possibility of identifying AE signatures of damage mechanisms is an established field [10; 11; 12; 13; 14; 15; 16]. The diagnostic phase consists of data acquisition, signal processing and features extraction and selection. The main diagnostic challenges in AE monitoring are related to the coexistence of AE signatures due to several damage mechanisms, as well as the numerous features that affect the original signal emitted by the AE source. The finally acquired AE signal is the convolution of the source signal, medium transfer function and sensor transfer function. In fact, AE signals depend on several distinct factors [17; 18; 19; 20; 21; 22; 23]: (1) the damage source, (2) the specimen geometry, (3) the material properties and (4) the type of sensor and instrumentation. The contribution of each of these elements must be considered to correctly analyze the recorded waveforms. Consequently, it is not straightforward to establish a quantitative link between the damage sources and AE signals, and further investigations are needed to enrich the existing experimental studies on this topic. Modeling and numerical simulations of AE appear as a promising way to reach this objective [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. The first works about modeling an AE source were based on analytic and semi-analytic methods.