## 1 Introduction  
The nonlinear mechanical characteristics of elastoplastic wet sand are ubiquitous in nature and allows it to be used in industrial processing, such as in feed industries [1], agricultural engineering, and biosystems engineering [2, 3, 4, 5, 6]. Elastoplastic wet sand formed through accumulation and cementation deviates from the fundamental assumption of continuum mechanics, making the discrete element method (DEM) the primary method for scholars to comprehend its properties. One critical bottleneck that impedes the development and deployment of the DEM is establishing precise mapping between macroscopic mechanical constitutive relationships and microscopic model parameters [7]. Moreover, numerous internal parameters such as the contact plasticity ratio cannot be determined through experimental measurements, and some of these parameters lack direct physical significance in elastoplastic mechanics. The accuracy of model parameters determines the confidence level of the DEM simulation results. Addressing the parameter identification in the DEM constitutes a prerequisite to overcome the aforementioned bottleneck. Parameter calibration [8; 9; 10; 11; 12; 13] is an effective method for addressing unidentifiability in parameters and their combinations, with optimization-based techniques being one common approach. The challenge in parameter calibration is identifying meaningful parameter combinations in high-dimensional and highly coupled parameter spaces that can accurately perform numerical simulations. [PERSON] and [PERSON] [14] define meaningful functionalities as those that strike a balance between accuracy and complexity. Model-driven and data-driven technologies [15] are two main research paradigms oriented to parameter calibration. The optimization efficiency of the former depends on the discrepancies between the predicted constitutive model and real systems. Due to the limited expressive ability of the models, the information from real systems is dimensionally reduced, resulting in the natural loss of information during the modeling process. Data-driven strategies use the inherent connections in the data to create more precise parameter combinations, which reduces the need for prior knowledge and enhances the interpretability of the models [16]. Data-driven technologies have advanced significantly during the past two decades along with the emergence of deep learning [17; 18]. [PERSON] et al. [19] demonstrated the generalization of deep learning by highlighting, with a sufficient number of neurons and layers, that deep learning can learn to accurately map any number of inputs to any number of outputs, enabling it to model nearly anything, including the parameter unidentifiability in the DEM. To achieve parameter calibration correctly, researchers have undertaken exploratory works in three aspects, as outlined in Figure 1. The first aspect involves theory-based methods, which utilize sensitivity analysis or the Plackett-Burman analysis [5] to reduce parameter dimensions. Subsequently, iterative cross-analysis [20], response surface methodology [21], adaptive moment estimation [22], and the least squares method [23] are employed for parameter calibration. However, theory-based methods have limited modeling capabilities for nonlinear systems. The assumptions and restrictions on the parameter space may lead to convergence on a solution domain that cannot cover all parameter combinations, making it difficult to achieve parallel calibration for a large number of parameters. The second aspect partially addresses these challenges by applying statistical optimization algorithms. [PERSON] et al. [24] proposed a parameter calibration method using nondominated Sorting Genetic Algorithm II, achieving a Pareto-optimal balance between the calibration accuracy and speed. [PERSON] et al. [25] pointed out the limitations of empirical methods in parameter calibration and proposed a new method based on an improved Particle Swarm Optimization (PSO) algorithm.