A software framework for probabilistic sensitivity analysis for computationally expensive models
We provide a sensitivity analysis toolbox consisting of a set of Matlab functions that offer
utilities for quantifying the influence of uncertain input parameters on uncertain model …
utilities for quantifying the influence of uncertain input parameters on uncertain model …
Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites
We present a stochastic integrated machine learning based multiscale approach for the
prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric …
prediction of the macroscopic thermal conductivity in carbon nanotube reinforced polymeric …
[HTML][HTML] Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression
F Khademi, SM Jamal, N Deshpande… - International Journal of …, 2016 - Elsevier
Compressive strength of concrete, recognized as one of the most significant mechanical
properties of concrete, is identified as one of the most essential factors for the quality …
properties of concrete, is identified as one of the most essential factors for the quality …
Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete
Evaluating the in situ concrete compressive strength by means of cores cut from hardened
concrete is acknowledged as the most ordinary method, however, it is very difficult to predict …
concrete is acknowledged as the most ordinary method, however, it is very difficult to predict …
[HTML][HTML] Methods for enabling real-time analysis in digital twins: A literature review
This paper presents a literature review on methods for enabling real-time analysis in digital
twins, which are virtual models of physical systems. The advantages of digital twins are …
twins, which are virtual models of physical systems. The advantages of digital twins are …
Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design
We propose a computational framework using surrogate models through five steps, which
can systematically and comprehensively address a number of related stochastic multi-scale …
can systematically and comprehensively address a number of related stochastic multi-scale …
[HTML][HTML] Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters
We propose a stochastic multiscale method to quantify the correlated key-input parameters
influencing the mechanical properties of polymer nanocomposites (PNCs). The variations of …
influencing the mechanical properties of polymer nanocomposites (PNCs). The variations of …
[HTML][HTML] Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden
This paper presents an open digital ecosystem based on a web-framework with a functional
back-end server for user-centric energy retrofits. This data-driven web framework is …
back-end server for user-centric energy retrofits. This data-driven web framework is …
Effect of interphase percolation on mechanical behavior of nanoparticle-reinforced polymer nanocomposite with filler agglomeration: A multiscale approach
The degradation mechanism of mechanical properties of a polymer nanocomposite
consisting of agglomerating fillers is elucidated. It is found that overall elastic moduli of …
consisting of agglomerating fillers is elucidated. It is found that overall elastic moduli of …
A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites
We propose a stochastic framework based on sensitivity analysis (SA) methods to quantify
the key-input parameters influencing the Young's modulus of polymer (epoxy) clay …
the key-input parameters influencing the Young's modulus of polymer (epoxy) clay …