Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[图书][B] Uncertainty quantification: theory, implementation, and applications
RC Smith - 2024 - SIAM
Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …
engineering, and biological applications using mechanistic models. From a broad …
A survey of projection-based model reduction methods for parametric dynamical systems
Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …
a wide range of complex physical phenomena; however, the inherent large-scale nature of …
A review of surrogate models and their application to groundwater modeling
MJ Asher, BFW Croke, AJ Jakeman… - Water Resources …, 2015 - Wiley Online Library
The spatially and temporally variable parameters and inputs to complex groundwater
models typically result in long runtimes which hinder comprehensive calibration, sensitivity …
models typically result in long runtimes which hinder comprehensive calibration, sensitivity …
A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses
Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-
order) aerodynamic models or flight testing are some of the fundamental but complex steps …
order) aerodynamic models or flight testing are some of the fundamental but complex steps …
A digital twin hierarchy for metal additive manufacturing
Digital twins present a conceptual framework for product life-cycle monitoring and control
using a simulated replica of the physical system. Since their emergence, they have garnered …
using a simulated replica of the physical system. Since their emergence, they have garnered …
A deep learning enabler for nonintrusive reduced order modeling of fluid flows
In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …
Simulation-based optimal Bayesian experimental design for nonlinear systems
X Huan, YM Marzouk - Journal of Computational Physics, 2013 - Elsevier
The optimal selection of experimental conditions is essential to maximizing the value of data
for inference and prediction, particularly in situations where experiments are time …
for inference and prediction, particularly in situations where experiments are time …