OPTIMIZATION OF DEEP LEARNING ACCELERATION OF TOTAL EXPLICIT DYNAMICS FOR SOFT TISSUE MECHANICS STUDY

Deep learning has emerged as a powerful tool for accelerating simulations in various scientific domains. In the field of soft tissue mechanics, explicit dynamics simulations play a crucial role in understanding the mechanical behaviour of biological tissues. However, these simulations are computationally intensive, often requiring significant computational resources and time. We propose an optimization approach for accelerating the deep learning-based simulation of total explicit dynamics in soft tissue mechanics. We leverage the capabilities of deep learning algorithms to improve the efficiency and speed of simulations without compromising accuracy. Our optimization framework consists of three main components: data preprocessing, model architecture, and training strategy. We pre-process the training data by applying dimensionality reduction techniques and feature selection to extract the most relevant information. This helps in reducing the complexity of the input data and enhances the training process. Then we design an optimized model architecture that leverages state-of-the-art deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms. This architecture is tailored to capture the underlying patterns and dynamics of soft tissue mechanics simulations, enabling accurate predictions with reduced computational overhead. Finally, we devise a novel training strategy that incorporates transfer learning, data augmentation, and regularization techniques. Transfer learning allows us to leverage pre-trained models on related tasks to initialize the network, which accelerates the convergence of training. Data augmentation techniques are employed to artificially increase the size of the training dataset, leading to better generalization and robustness of the model. Regularization techniques are utilized to prevent overfitting and improve the model's ability to generalize to unseen data. We evaluate our optimized deep learning approach .n a comprehensive dataset of soft tissue mechanics simulations and compare it with traditional explicit dynamics simulations. Our results demonstrate significant improvements in computational efficiency, with up to a reduction in simulation time while maintaining high accuracy levels.