Adv Water Resour 2015 Apr;78:60-79

Department of Civil & Environmental Engineering & Earth Sciences, University of Notre Dame, South Bend, IN.

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

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http://dx.doi.org/10.1016/j.advwatres.2015.01.011 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4415439 | PMC |

Annu Rev Vis Sci 2021 Aug 4. Epub 2021 Aug 4.

Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA; email:

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. Read More

IEEE J Biomed Health Inform 2021 Aug 4;PP. Epub 2021 Aug 4.

A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of an indicator matrix; that is, in terms of the inverse problem of regressing the observations. Both approaches, i. Read More

Psychiatry 2021 Aug 2:1-15. Epub 2021 Aug 2.

: Studies examining posttraumatic growth (PTG) rely on surveys evaluating PTG in relation to prior traumatic experiences, resulting in psychometric problems due to the linkage of the dependent and independent variables. Few studies have assessed PTG following combat deployment while also controlling for mental health problems.: Longitudinal data on PTG, combat experience, and mental health were examined among U. Read More

Rev Sci Instrum 2021 Jul;92(7):075003

EE Department, IIT Kanpur, ACES 329, Kanpur, India.

An artificial neural network (ANN) based tunable automated standalone RF sensor system is proposed to realize an improved sensing module involving a quite accurate solution of the non-linear inverse characterization problem. The presented tunable sensor system is quite novel as it alleviates the need for any active tuning circuitry. Moreover, the proposed unified design topology facilitates a relatively higher tuning range (1900 MHz) than that of the earlier reported (580 MHz) capacitor-based tunable complementary split-ring resonator (CSRR). Read More

Comput Intell Neurosci 2021 14;2021:9943519. Epub 2021 Jul 14.

New Huadu Business School of Minjiang University, Minjiang University, Fuzhou 350108, China.

The penalty decomposition method is an effective and versatile method for sparse optimization and has been successfully applied to solve compressed sensing, sparse logistic regression, sparse inverse covariance selection, low rank minimization, image restoration, and so on. With increase in the penalty parameters, a sequence of penalty subproblems required being solved by the penalty decomposition method may be time consuming. In this paper, an acceleration of the penalty decomposition method is proposed for the sparse optimization problem. Read More

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