Events

Abtract: Characterization and modeling of complex materials by phenomenological models remains challenging due to difficulties in formulating mathematical expressions and internal state variables (ISVs) governing path-dependent behaviors. Data-driven machine learning models, such as deep neural networks and recurrent neural networks (RNNs), have become viable alternatives. However, pure black-box data-driven models mapping inputs to outputs without considering the underlying physics suffer from unstable and inaccurate generalization performance. This study proposes a machine-learned physics-informed data-driven constitutive modeling approach for path-dependent materials based on the measurable material states. The proposed data-driven constitutive model is designed with the consideration of universal thermodynamics principles, where the ISVs essential to the material path-dependency are inferred automatically from the hidden state of RNNs. For materials subjected to fracturing or strain localization, a neural network enriched Galerkin solution, called the N-Adaptive Ritz Method, for weak and strong discontinuities and for adaptive refinement without re-meshing is introduced. These unique combinations of machine learning techniques and advanced computational methods have expanded the horizon of computational mechanics and scientific computing beyond what the conventional computational methods can offer. Applications to plasticity, localization, fracture, thermal fatigue, and digital twins will be presented to demonstrate the effectiveness of these new developments for computational mechanics.

Bio: J. S. Chen is the William Prager Chair Professor and Distinguished Professor of Structural Engineering Department, Mechanical & Aerospace Engineering Department, and the Founding Director of Center for Extreme Events Research at UC San Diego. J. S. Chen’s research is in computational mechanics and multiscale materials modeling with specialization in the development of meshfree methods. He is the Past President of US Association for Computational Mechanics (USACM) and the Past President of ASCE Engineering Mechanics Institute (EMI). He has received numerous awards, including the Computational Mechanics Award from International Association for Computational Mechanics (IACM), the Grand Prize from Japan Society for Computational Engineering and Science (JSCES), the Ted Belytschko Applied Mechanics Award from ASME Applied Mechanics Division, the Belytschko Medal from U.S. Association for Computational Mechanics (USACM), the Computational Mechanics Award from Japan Association for Computational Mechanics (JACM), the ICACM Award from International Chinese Association for Computational Mechanics (ICACM), among others. He received BS (Civil Engineering) from National Central University, Taiwan, and MS and PhD (Theoretical & Applied Mechanics) from Northwestern University.