Mechanical and Civil Engineering Seminar

Title: "What a Digital Twin Can Learn from Data, Thermodynamics, and Action Principles for Damage Assessment in Complex Materials"

Abstract:

This talk explores the interplay between data, thermodynamics, and action principles in the development of thermodynamics‑aware digital twins for damage assessment in complex materials microstructures. Data is leveraged in two key ways: image data is used to represent as-built material microstructures, while measurable material state data supports data-driven computation. The link between microstructure, material state, and structural damage response is grounded in thermodynamic principles and the principle of least action. The Support Vector Machine (SVM) algorithm is employed for automatic microstructure segmentation, enabling direct model discretization from image pixels without the need for body-fitted mesh generation. Inelastic material behavior is modeled in a purely data-driven manner, bypassing traditional constitutive models that often lack generalizability across loading conditions. To capture localized damage and microstructural features with coarse discretization, we introduce neural network (NN) enrichment of the RKPM framework. The NN approximation is formulated through energy minimization, with optimal parameters encoding the location, orientation, and transition behavior of damage zones. Regularization ensures discretization-independent solutions, and convergence properties are analytically derived and numerically verified. For transient dynamics, the NN-enriched formulation is based on action minimization and symplectic integration, yielding solutions consistent with classical field theory. The effectiveness of this digital twin framework is demonstrated in modeling damage evolution in composite materials and structures, and comparison with experimental results validated the accuracy and reliability of the proposed computational framework.

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 University of California San Diego (UCSD). Before joining UCSD in 2013, he was the Chancellor's Professor of UCLA Civil & Environmental Engineering Department, Mechanical & Aerospace Engineering Department, and Mathematics Department, where he served as the Department Chair of Civil & Environmental Engineering during 2007-2012. J. S. Chen's research is in computational mechanics, meshfree methods, multiscale materials modeling, machine-learning-enhanced computational mechanics, and physics-informed data-driven computing. 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 John von Neumann Medal from the US Association for Computational Mechanics (USACM), the Belytschko Medal from USACM, the Raymond D. Mindlin Medal from ASCE EMI, the Computational Mechanics Award from the 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 Computational Mechanics Award from Japan Association for Computational Mechanics (JACM), the ICACM Award from International Chinese Association for Computational Mechanics (ICACM), among others. He is the Fellow of USACM, IACM, ASME, EMI, SES, ICACM, and ICCEES. He received PhD in Theoretical & Applied Mechanics from Northwestern University.