Graph Neural Network-based Surrogate Models for Finite Element Analysis
Meduri Venkata Shivaditya, Jose Alves, Francesca Bugiotti, Frédéric Magoulès
Abstract
Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Network-based graph prediction model to act as a surrogate model for parameters search space exploration and which exhibits a time cost reduced by an order of magnitude. Numerical experiments show that this new model outperforms the Point-Net model and the Dynamic Graph Convolutional Neural Net model.
Topics & Concepts
Computer scienceArtificial neural networkParametric statisticsFinite element methodSurrogate modelGraphConvolutional neural networkAlgorithmMathematical optimizationArtificial intelligenceTheoretical computer scienceMachine learningMathematicsThermodynamicsPhysicsStatisticsMachine Learning in Materials ScienceManufacturing Process and OptimizationMineral Processing and Grinding