P55 - Novel Geometric Deep Learning Surrogate Framework for Non-Linear Finite Element Simulations
DescriptionConventional numerical methods are computationally expensive in simulating non-linear phenomena arising in mechanics. In this aspect, deep learning (DL) techniques are being increasingly used for accelerating simulations in mechanics. However, existing DL methods perform inefficiently as the size and complexity of the problem increases. In this work we propose a novel geometric deep learning surrogate framework, which can efficiently find non-linear mappings between mesh-based datasets. In particular, we propose two novel neural network layers, Multichannel Aggregation (MAg) layer, and the graph pooling layer, which are combined to constitute a robust graph U-Net architecture. Our framework can efficiently tackle problems involving complex fine meshes and scales efficiently to large dimensional inputs. We validate the performance of our framework by learning on numerically generated non-linear finite element datasets and by comparing the performance to state-of-the-art convolutional neural network frameworks. In particular, we show that the proposed GDL framework is able to accurately predict the nonlinear deformations of irregular soft bodies in real-time.
TimeTuesday, June 2719:30 - 21:30 CEST