Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Faghihi, Danial"'
The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility
Externí odkaz:
http://arxiv.org/abs/2403.08901
Autor:
Tan, Jingye, Faghihi, Danial
This work presents a scalable computational framework for optimal design under uncertainty with application to multi-material insulation components of building envelopes. The forward model consists of a multi-phase thermo-mechanical model of porous m
Externí odkaz:
http://arxiv.org/abs/2304.01139
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-dependent reaction-diffusion partial differential equation (PDE) with
Externí odkaz:
http://arxiv.org/abs/2302.06445
Autor:
Liang, Baoshan, Tan, Jingye, Lozenski, Luke, Hormuth II, David A., Yankeelov, Thomas E., Villa, Umberto, Faghihi, Danial
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 growth, and spatial heterogeneity of tumor and host tissue. This work int
Externí odkaz:
http://arxiv.org/abs/2209.12089
Autor:
Tan, Jingye, Maleki, Pedram, An, Lu, Di Luigi, Massimigliano, Villa, Umberto, Zhou, Chi, Ren, Shenqiang, Faghihi, Danial
This work develops a multiphase thermomechanical model of porous silica aerogel and implements an uncertainty analysis framework consisting of the Sobol methods for global sensitivity analyses and Bayesian inference using a set of experimental data o
Externí odkaz:
http://arxiv.org/abs/2107.12182
Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundamental limitation in multiscale models is the presence of uncertainty in
Externí odkaz:
http://arxiv.org/abs/2012.10823
Autor:
Tan, Jingye, Faghihi, Danial
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 February 2024 419
Publikováno v:
In International Journal of Heat and Mass Transfer 15 November 2023 215
Autor:
Li, Zheng, Tangudu, Jagadeesh, Saviers, Kimberly, Singh, Pratyush Kumar, Islam, Abdullah, Faghihi, Danial, Ren, Shenqiang
Publikováno v:
In Applied Materials Today August 2023 33
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 modeling error in key quantities of interest (QoIs). For specificity, the
Externí odkaz:
http://arxiv.org/abs/1808.01923