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

Eur Radiol 2022 Aug 18. Epub 2022 Aug 18.

Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Strasse 22, 81675, Munich, Germany.

*Objectives*: To evaluate the performance of single-echo Dixon water-fat imaging and computed tomography (CT)-like imaging based on a single ultrashort echo time (sUTE) MR sequence for imaging of vertebral fractures as well as degenerative bone changes of the spine in comparison to conventional CT and MR sequences.*Methods*: Thirty patients with suspected acute vertebral fractures were examined using a 3-T MRI, including an sUTE sequence as well as short-tau inversion recovery (STIR) and T1-weighted sequences. During postprocessing, water-fat separation was performed by solving the smoothness-constrained inverse water-fat problem based on a single-complex UTE image. Read More

Mater Adv 2022 Aug 24;3(15):6280-6290. Epub 2022 Jun 24.

Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge MA 02139 USA

Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design. However, the availability of proper data remains a challenge - often, data lacks labels, or does not contain direct pairing between input and output property of interest. Here we report an approach based on an adversarial neural network model - composed of four individual deep neural nets - to yield atomistic-level prediction of stress fields directly from an input atomic microstructure, illustrated here for defected graphene sheets under tension. Read More

Biomed Tech (Berl) 2022 Aug 19. Epub 2022 Aug 19.

Department of Mechatronics Engineering, Sakarya University of Applied Sciences, Serdivan, Sakarya, Turkey.

The prevalence of obesity, a worldwide health problem, is increasing. Obesity or overweight has significant effects, especially on lower limb biomechanics. Previous studies have investigated the biomechanical effects of weight gain on the knee and hip joints. Read More

J Appl Crystallogr 2022 Aug 1;55(Pt 4):966-977. Epub 2022 Aug 1.

Scientific Computing Department, STFC, Rutherford Appleton Laboratory, Didcot OX11 0QX, United Kingdom.

A general method to invert parameter distributions of a polydisperse system using data acquired from a small-angle scattering (SAS) experiment is presented. The forward problem, calculating the scattering intensity given the distributions of any causal parameters of a theoretical model, is generalized as a multi-linear map, characterized by a high-dimensional Green tensor that represents the complete scattering physics. The inverse problem, finding the maximum-likelihood estimation of the parameter distributions (in free form) given the scattering intensity (either a curve or an image) acquired from an experiment, is formulated as a constrained nonlinear programming (NLP) problem. Read More

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