Physics-informed neural networks for modeling rate- and temperature-dependent plasticity

This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids. To avoid unbalanced back-propagated gradients during training, the proposed framework uses a simple strategy with no added computational complexity for selecting scalar weights that balance the interplay between different terms in the physics-based loss function. In addition, we highlight a fundamental challenge involving the selection of appropriate model outputs so that the mechanical problem can be faithfully solved using a PINN-based approach. We demonstrate the effectiveness of this approach by studying two test problems modeling the elastic-viscoplastic deformation in solids at different strain rates and temperatures, respectively. Our results show that the proposed PINN-based approach can accurately predict the spatio-temporal evolution of deformation in elastic-viscoplastic materials.Comment: 11 pages, 7 figures; Accepted in NeurIPS 2022, Machine Learning and the Physical Sciences worksho

Load-Induced Crack Prediction Using Hybrid Mechanistic Machine Learning Models

This thesis implements hybrid mechanistic-machine learning models to predict load-induced cracking in concrete beams without transverse (applied along the depth of the beam) reinforcement. Predicting load-induced cracking is crucial for robustly predicting shear capacity. Mechanistic models lack the flexibility to represent load-induced cracking, and machine learning models lack a sufficient data set to learn load-induced cracking relationships. Hybrid models have the best chance of accurately predicting load-induced cracking. To implement hybrid modeling, we developed the Hybrid Learning theory and identified optimal combinations of mechanistic and machine learning models. Additionally, we developed a framework that has low mechanistic bias and sufficient constraint. This framework will allow for mechanistically consistent predictions. Hybrid models have great potential for modeling in Structural Engineering because of their flexibility and interpretability, and robust prediction of shear capacity will lead to increased design efficiency and understanding of concrete beam failure mechanics