1 citations · 3 across the 13 of their papers we have counts for
14 papers
Dynamic Image-Informed Selection of Biomechanical Tumor Growth Models
Abdullah Al Noman, Pratyush Kumar Singh, David A Hormuth +1
Glioblastoma progression is strongly influenced by evolving mechanical interactions between the tumor and surrounding brain tissue. However, the extent to which finite-deformation…
Evidence-Guided Neural Architecture Selection under Uncertainty for Subject-Specific Blood Glucose Forecasting
Md Azharul Islam, Dwyer Deighan, Tarunraj Singha +1
Reliable neural architecture selection is an open challenge in time-series forecasting under limited, noisy, and heterogeneous data, where standard heuristic architecture design an…
Stochastic Deep Learning Surrogate Models for Uncertainty Propagation in Microstructure-Properties of Ceramic Aerogels
Md Azharul Islam, Dwyer Deighan, Shayan Bhattacharjee +4
This study presents an integrated computational framework that, given synthesis parameters, predicts the resulting microstructural morphology and mechanical response of ceramic aer…
Chance-Constrained Optimal Design of Porous Thermal Insulation Systems Under Spatially Correlated Uncertainty
Pratyush Kumar Singh, Danial Faghihi
This paper presents a computationally efficient method for the optimal design of silica aerogel porous material systems, balancing thermal insulation performance with mechanical st…
UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration
Georgios Georgalis, Alejandro Becerra, Kenneth Budzinski +4
The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based s…
A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty
Pratyush Kumar Singh, Kathryn A. Farrell-Maupin, Danial Faghihi
The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical nece…
A Scalable Algorithm for Multi-Material Design of Thermal Insulation Components Under Uncertainty
Jingye Tan, Danial Faghihi
This work presents a scalable computational framework for optimal design under uncertainty with application to multi-material insulation components of building envelopes. The forwa…
Technical Note: PDE-constrained Optimization Formulation for Tumor Growth Model Calibration
Baoshan Liang, Luke Lozenski, Umberto Villa +1
We discuss solution algorithms for calibrating a tumor growth model using imaging data posed as a deterministic inverse problem. The forward model consists of a nonlinear and time-…
Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth
Baoshan Liang, Jingye Tan, Luke Lozenski +4
Reliably predicting the future spread of brain tumors using imaging data and on a subject-specific basis requires quantifying uncertainties in data, biophysical models of tumor gro…
A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations
Jingye Tan, Pedram Maleki, Lu An +5
This work develops a multiphase thermomechanical model of porous silica aerogel and implements an uncertainty analysis framework consisting of the Sobol methods for global sensitiv…
A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty
Jingye Tan, Umberto Villa, Nima Shamsaei +3
Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundame…
Goal-Oriented Adaptive Modeling of Random Heterogeneous Media and Model-Based Multilevel Monte Carlo Methods
Laura Scarabosio, Barbara Wohlmuth, J. Tinsley Oden +1
Methods for generating sequences of surrogates approximating fine scale models of two-phase random heterogeneous media are presented that are designed to adaptively control the mod…
A Probabilistic Design Method for Fatigue Life of Metallic Component
Danial Faghihi, Subhasis Sarkar, Mehdi Naderi +2
In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncertainty in experi…
Moment Preserving Constrained Resampling with Applications to Particle-in-Cell Methods
Danial Faghihi, Varis Carey, Craig Michoski +4
In simulations of partial differential equations using particle-in-cell (PIC) methods, it is often advantageous to resample the particle distribution function to increase simulatio…