Research Output per year

## Fingerprint Fingerprint is based on mining the text of the expert's scholarly documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

- 2 Similar Profiles

Chaos theory
Engineering & Materials Science

chaos
Physics & Astronomy

polynomials
Physics & Astronomy

Polynomials
Engineering & Materials Science

Structural dynamics
Engineering & Materials Science

Markov processes
Engineering & Materials Science

expansion
Physics & Astronomy

Sampling
Engineering & Materials Science

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Network
Recent external collaboration on country level. Dive into details by clicking on the dots.

## Research Output 2009 2018

## A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions

Alemazkoor, N. & Meidani, H. Oct 15 2018 In : Journal of Computational Physics. 371, p. 137-151 15 p.Research output: Contribution to journal › Article

Chaos theory

chaos

polynomials

recovery

sampling

## Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

Nabian, M. A. & Meidani, H., Jun 1 2018, In : Computer-Aided Civil and Infrastructure Engineering. 33, 6, p. 443-458 16 p.Research output: Contribution to journal › Article

reliability analysis

Reliability analysis

connectivity

learning

infrastructure

## Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Wu, X., Kozlowski, T., Meidani, H. & Shirvan, K. Aug 15 2018 In : Nuclear Engineering and Design. 335, p. 339-355 17 p.Research output: Contribution to journal › Article

formulations

Bayesian analysis

nuclear reactors

prediction

guy wires

## Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE

Wu, X., Kozlowski, T., Meidani, H. & Shirvan, K. Aug 15 2018 In : Nuclear Engineering and Design. 335, p. 417-431 15 p.Research output: Contribution to journal › Article

Probability distributions

Uncertainty

test

Void fraction

self assessment

## Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data

Wu, X., Kozlowski, T. & Meidani, H. Jan 1 2018 In : Reliability Engineering and System Safety. 169, p. 422-436 15 p.Research output: Contribution to journal › Article

Nuclear fuels

Time series

Gases

Nuclear reactors

Principal component analysis