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542 results on '"Oberai, Assad"'

108. Material parameters for different models from Hyperelastic continuum models for isotropic athermal fibrous networks

Author

Song, Dawei, Oberai, Assad A, and Janmey, Paul A

Abstract

Many biological materials contain fibrous protein networks as their main structural components. Understanding the mechanical properties of such networks is important for creating biomimicking materials for cell and tissue engineering, and for developing novel tools for detecting and diagnosing disease. In this work, we develop continuum models for isotropic, athermal fibrous networks by combining a single-fibre model that describes the axial response of individual fibres, with network models that assemble individual fibre properties into overall network behaviour. In particular, we consider four different network models, including the affine, three-chain, eight-chain and micro-sphere models, which employ different assumptions about network structure and kinematics. We systematically investigate the ability of these models to describe the mechanical response of athermal collagen and fibrin networks by comparing model predictions with experimental data. We test how each model captures network behaviour under three different loading conditions: uniaxial tension, simple shear and combined tension and shear. We find that the affine and three-chain models can accurately describe both the axial and shear behaviour, whereas the eight-chain and micro-sphere models fail to capture the shear response, leading to an unphysical zero shear moduli at infinitesimal strains. Our study is the first to systematically investigate the applicability of popular network models for describing the macroscopic behaviour of athermal fibrous networks, offering insights for selecting efficient models that can be used for large-scale, finite-element simulations of athermal networks.

Published
2022
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127. Computation of trailing-edge noise due to turbulent flow over an airfoil

Author

Oberai, Assad A., Roknaldin, Farzam, and Hughes, Thomas J.R.

Subjects
Aerofoils -- Analysis, Noise control -- Methods, Turbulence -- Analysis, Aerospace and defense industries, Business
Abstract

Application of the variational formulation of Lighthill's acoustic analogy to trailing-edge noise is considered. Use is made of this formulation to study the effect of finiteness of the chord and the variation of far-field pressure directivity with frequency. Numerical analytical solution results are validated for certain limiting cases. Use is also made of this methodology to calculate the far-field acoustic pressure for a low-Mach-number turbulent flow. To determine the acoustic sources for this problem, we employ an unstructured mesh, large eddy simulation of the incompressible Navier-Stokes equations.

Published
2002

133. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses

Author

Yap, Felix Y., primary, Varghese, Bino A., additional, Cen, Steven Y., additional, Hwang, Darryl H., additional, Lei, Xiaomeng, additional, Desai, Bhushan, additional, Lau, Christopher, additional, Yang, Lindsay L., additional, Fullenkamp, Austin J., additional, Hajian, Simin, additional, Rivas, Marielena, additional, Gupta, Megha Nayyar, additional, Quinn, Brian D., additional, Aron, Manju, additional, Desai, Mihir M., additional, Aron, Monish, additional, Oberai, Assad A., additional, Gill, Inderbir S., additional, and Duddalwar, Vinay A., additional

Published
2020
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140. GAN-based Priors for Quantifying Uncertainty

Author

Dhruv V Patel and Oberai, Assad A

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, Machine Learning (stat.ML), Computational Physics (physics.comp-ph), Physics - Computational Physics, Machine Learning (cs.LG)
Abstract

Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior distributions that are difficult to characterize mathematically. In this work we demonstrate how the approximate distribution learned by a deep generative adversarial network (GAN) may be used as a prior in a Bayesian update to address both these challenges. We demonstrate the efficacy of this approach on two distinct, and remarkably broad, classes of problems. The first class leads to supervised learning algorithms for image classification with superior out of distribution detection and accuracy, and for image inpainting with built-in variance estimation. The second class leads to unsupervised learning algorithms for image denoising and for solving physics-driven inverse problems.

Published
2020
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