## About

22

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102

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Introduction

## Publications

Publications (22)

The ability to make decisions based on quantities of interest that depend on variables inferred from measurement finds application in different fields of mechanics and physics. The evaluation of the inferred variables, and hence the quantities of interest, from the measurement typically requires the solution of an inverse problem. For example, in m...

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 distributi...

Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical...

Defects are ubiquitous in semiconductor industry, and their detection, classification, and localization at various levels is a challenge that requires rigorous sampling. Current state-of-the art optical and e-beam inspection systems based on gray-scale differences for detection and rule-based binning for classification are rigid and are not invaria...

Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach for overcoming this by formula...

Operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input functions that describe material properties, forcing functions and boundary data to the solution of a PDE. This work describes a new architecture for operator networks that mimics the form...

The ability to impute missing images from a sequence of medical images plays an important role in enabling the detection, diagnosis and treatment of disease. Motivated by this, in this manuscript we propose a novel, probabilistic deep-learning algorithm for imputing images. Within this approach, given a sequence of contrast enhanced CT images, we t...

In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems. The generator is constructed using a U-Net architecture, with the latent information injected using conditional instance normalization. The former facilitates a multiscale inverse map,...

Inverse problems are notoriously difficult to solve because they can have no solutions, multiple solutions, or have solutions that vary significantly in response to small perturbations in measurements. Bayesian inference, which poses an inverse problem as a stochastic inference problem, addresses these difficulties and provides quantitative estimat...

Contrast-Enhanced CT (CECT) imaging is used in the diagnosis of renal cancer and planning of surgery. A sequence of CECT phase images which captures the movement of contrast agent inside an organ often completely misses some of the phase images or has corrupted phase images making the entire sequence useless. We propose a probabilistic deep generat...

Generative adversarial networks (GANs) have found multiple applications in the solution of inverse problems in science and engineering. These applications are driven by the ability of these networks to learn complex distributions and to map the original feature space to a low-dimensional latent space. In this manuscript we consider the use of GANs...

Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical...

Defects in semiconductor processes can limit yield, increase overall production cost, and also lead to time-dependent critical component failures. Current state-of-the-art optical and electron beam (EB) inspection systems rely on rule-based techniques for defect detection and classification, which are usually rigid in their comparative processes. T...

The solution of an inverse problem involves using a measured field to infer the field of interest, where the two are connected through a well-defined forward operator. For example, the measured field might be the temperature of a solid and the field to be inferred might be the spatial variation of its thermal conductivity. Most inverse problems are...

Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a physical model. Despite its many applications, Bayesian inference faces challenges when inferring fields that have discrete representations of large dimension, and/or have prior di...

The ability to make decisions based on quantities of interest that depend on variables inferred from measurement finds application in different fields of mechanics and physics. The evaluation of the inferred variables, and hence the quantities of interest, from the measurement typically requires the solution of an inverse problem. For example, in m...

Final project 2018 Gene Golub SIAM Summer School : Inverse Problems: Systematic Integration of Data with Models under Uncertainty

Attainment of the understanding of the way in which highly compacted DNA in cell-nucleus is made available for different biological processes is an exciting mechanics problem. The work presented over here is aimed to obtain different equilibrium
configurations of a super coiled DNA and to understand how buckling force varies with length and twist...

## Projects

Project (1)