Convolutional neural networks (CNNs) find vast applications in the field of image
processing. This study utilises the CNNs in conjunction with the crystal plasticity finite …

For material modeling and discovery, synthetic microstructures play a critical role as digital
twins. They provide stochastic samples upon which direct numerical simulations can be …

Porous membranes have been utilized intensively in a wide range of fields due to their
special characteristics and a rigorous characterization of their microstructures is crucial for …

A recently developed measure-theoretic framework solves a stochastic inverse problem
(SIP) for models where uncertainties in model output data are predominantly due to aleatoric …

This work aims to investigate complex relationship between microstructure characteristics
and mechanical properties of dual phase (DP) steel through an inverse analysis based on …

Quantifying uncertainty associated with the microstructure variation of a material can be a
computationally daunting task, especially when dealing with advanced constitutive models …

Inverse material design is a cornerstone challenge in materials science, with significant
applications across many industries. Traditional approaches that invert the structure …

The three-dimensional microstructure of functional materials determines its effective
properties, like the mass transport properties of a porous material. Hence, it is desirable to …

While the forward and backward modeling of the process-structure-property chain has
received a lot of attention from the materials' community, fewer efforts have taken into …

Uncertainty quantification (UQ) plays a major role in verification and validation for
computational engineering models and simulations, and establishes trust in the predictive …