5 papers
Process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and surrogate-based sensitivity analysis
Ethan Blackwell, Yogesh C. Chandrashekar, Guoqiang Li +1
Digital light processing (DLP) enables rapid fabrication of polymer structures, but fracture performance depends on multiple interacting processing variables, making exhaustive exp…
History Matters: Damage-Mediated Amplification of Brain Deformation and Injury Risk under Repeated Head Impacts
Carson Cooper, Anu Tripathi, Genevieve Palardy +1
Computational head models are typically applied to isolated impacts, leaving repeated head loading largely unexplored. An Ogden-Roxburgh Mullins damage formulation was implemented…
Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastography
Boyuan Deng, Kshitiz Upadhyay, Michael Shields
The Helmholtz equation governs time-harmonic wave propagation, and in dissipative media a complex modulus renders its squared wavenumber complex. Inferring such fields from s…
Stress Softening Damage in Strongly Nonlinear Viscoelastic Soft Materials A Physics Informed Data Driven Constitutive Model with Time Temperature Coupling
Alireza Ostadrahimi, Amir Teimouri, Kshitiz Upadhyay +1
This study presents a novel physics informed, data-driven modeling framework for capturing the strongly nonlinear thermo-viscoelastic behavior of soft materials exhibiting stress s…
A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading
Alireza Ostadrahimi, Amir Teimouri, Kshitiz Upadhyay +1
We propose a general hybrid physics-informed machine learning framework for modeling nonlinear, history-dependent viscoelastic behavior under multiaxial cyclic loading. The approac…