Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
Engineering analysis with probability boxes: A review on computational methods
The consideration of imprecise probability in engineering analysis to account for missing,
vague or incomplete data in the description of model uncertainties is a fast-growing field of …
vague or incomplete data in the description of model uncertainties is a fast-growing field of …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey
J Zhang - Wiley Interdisciplinary Reviews: Computational …, 2021 - Wiley Online Library
Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
Uncertainty quantification in multiscale simulation of woven fiber composites
Woven fiber composites have been increasingly employed as light-weight materials in
aerospace, construction, and transportation industries due to their superior properties …
aerospace, construction, and transportation industries due to their superior properties …
Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation
Uncertainty propagation through the simulation models is critical for computational
mechanics engineering to provide robust and reliable design in the presence of polymorphic …
mechanics engineering to provide robust and reliable design in the presence of polymorphic …
An efficient and robust sampler for Bayesian inference: Transitional ensemble Markov chain Monte Carlo
Bayesian inference is a popular approach towards parameter identification in engineering
problems. Such technique would involve iterative sampling methods which are often robust …
problems. Such technique would involve iterative sampling methods which are often robust …
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach
High entropy alloys (HEAs) are a series of novel materials that demonstrate many
exceptional mechanical properties. To understand the origin of these attractive properties, it …
exceptional mechanical properties. To understand the origin of these attractive properties, it …
Robust data-driven approach for predicting the configurational energy of high entropy alloys
High entropy alloys (HEAs) are promising next-generation materials due to their various
excellent properties. To understand these properties, it's necessary to characterize the …
excellent properties. To understand these properties, it's necessary to characterize the …
Reliability-oriented sensitivity analysis in presence of data-driven epistemic uncertainty
Reliability assessment in presence of epistemic uncertainty leads to consider the failure
probability as a quantity depending on the state of knowledge about uncertain input …
probability as a quantity depending on the state of knowledge about uncertain input …