Nested sampling for materials
We review the materials science applications of the nested sampling (NS) method, which
was originally conceived for calculating the evidence in Bayesian inference. We describe …
was originally conceived for calculating the evidence in Bayesian inference. We describe …
Model selection and sensitivity analysis in the biomechanics of soft tissues: A case study on the human knee meniscus
Soft tissues—such as ligaments and tendons—primarily consist of solid (collagen,
predominantly) and liquid phases. Understanding the interaction between such components …
predominantly) and liquid phases. Understanding the interaction between such components …
StocIPNet: A novel probabilistic interpretable network with affine-embedded reparameterization layer for high-dimensional stochastic inverse problems
J Mo, WJ Yan - Mechanical Systems and Signal Processing, 2024 - Elsevier
The stochastic inverse problem (StocIP), which aims to align push-forward and observed
output distributions by estimating probability distributions of unknown system inputs, often …
output distributions by estimating probability distributions of unknown system inputs, often …
Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network
Abstract We develop a Bayesian Inference (BI) of the parameters of a non-linear multiscale
model and of its material constitutive laws using experimental composite coupon tests as …
model and of its material constitutive laws using experimental composite coupon tests as …
The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions
The increased availability of observation data from engineering systems in operation poses
the question of how to incorporate this data into finite element models. To this end, we …
the question of how to incorporate this data into finite element models. To this end, we …
Deep learning in frequency domain for inverse identification of nonhomogeneous material properties
The inverse identification of nonhomogeneous material properties from measured
displacement/strain fields, especially when noise exists, is crucial for both engineering and …
displacement/strain fields, especially when noise exists, is crucial for both engineering and …
Bayesian inference of high-dimensional finite-strain visco-elastic–visco-plastic model parameters for additive manufactured polymers and neural network based …
In this work, the parameters of a finite-strain visco-elastic–visco-plastic formulation with
pressure dependency in both the visco-elastic and visco-plastic parts are identified using as …
pressure dependency in both the visco-elastic and visco-plastic parts are identified using as …
[HTML][HTML] Resolving engineering challenges: Deep learning in frequency domain for 3D inverse identification of heterogeneous composite properties
The inverse identification of heterogeneous composite properties from measured
displacement/strain fields is pivotal in engineering. Traditional methodologies and emerging …
displacement/strain fields is pivotal in engineering. Traditional methodologies and emerging …
Parameter identification and model updating in the context of nonlinear mechanical behaviors using a unified formulation of the modified constitutive relation error …
The motivation of this work is to propose a general methodology to deal with complex
nonlinear mechanical behaviors in the context of identification and model updating …
nonlinear mechanical behaviors in the context of identification and model updating …
A Bayesian method for selecting data points for thermodynamic modeling of off-stoichiometric metal oxides
Thermodynamic characterization of metal oxide reduction/re-oxidation plays a vital role in
material identification and optimization of many chemical processes. However, this …
material identification and optimization of many chemical processes. However, this …