Mesoscopic and multiscale modelling in materials
The concept of multiscale modelling has emerged over the last few decades to describe
procedures that seek to simulate continuum-scale behaviour using information gleaned from …
procedures that seek to simulate continuum-scale behaviour using information gleaned from …
[HTML][HTML] A state-of-the-art review on uncertainty analysis of rotor systems
Uncertainty handling and analysis of rotor systems have been active research areas during
the last two decades. This paper provides a state-of-the-art review on the research progress …
the last two decades. This paper provides a state-of-the-art review on the research progress …
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …
combine data with mathematical laws in physics and engineering in a profound way …
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification
State-of-the-art computer codes for simulating real physical systems are often characterized
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …
by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with …
Quantification of model uncertainty in RANS simulations: A review
In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …
Static homotopy response analysis of structure with random variables of arbitrary distributions by minimizing stochastic residual error
The modelling of realistic engineering structures with uncertainties often involves various
probabilistic distribution types, which bring forward higher requirements for the generality of …
probabilistic distribution types, which bring forward higher requirements for the generality of …
Deep learning of subsurface flow via theory-guided neural network
Active researches are currently being performed to incorporate the wealth of scientific
knowledge into data-driven approaches (eg, neural networks) in order to improve the latter's …
knowledge into data-driven approaches (eg, neural networks) in order to improve the latter's …
Stochastic model predictive control: An overview and perspectives for future research
A Mesbah - IEEE Control Systems Magazine, 2016 - ieeexplore.ieee.org
Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …
performance control of complex systems. The conceptual simplicity of MPC as well as its …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
[BUKU][B] Introduction to uncertainty quantification
TJ Sullivan - 2015 - books.google.com
This text provides a framework in which the main objectives of the field of uncertainty
quantification (UQ) are defined and an overview of the range of mathematical methods by …
quantification (UQ) are defined and an overview of the range of mathematical methods by …