Review of multi-fidelity models
MG Fernández-Godino - arxiv preprint arxiv:1609.07196, 2016 - arxiv.org
This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …
to the level of detail and accuracy provided by a predictive model or simulation. Generally …
Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization
Leave-one-out cross-validation (LOOCV) is a widely used technique in model estimation
and selection of the Kriging surrogate model for engineering problems, such as structural …
and selection of the Kriging surrogate model for engineering problems, such as structural …
Multi-fidelity surrogate modeling using long short-term memory networks
When evaluating quantities of interest that depend on the solutions to differential equations,
we inevitably face the trade-off between accuracy and efficiency. Especially for …
we inevitably face the trade-off between accuracy and efficiency. Especially for …
Hyperparameter-optimized multi-fidelity deep neural network model associated with subset simulation for structural reliability analysis
The present study proposes a two-stage Bi-Fidelity Deep Neural Network surrogate model to
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …
[PDF][PDF] Disentangled multi-fidelity deep bayesian active learning. In International Conference on Machine Learning
To balance quality and cost, various domain areas of science and engineering run
simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a …
simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a …
On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks
Abstract Deep Neural Network (DNN) is widely used in engineering applications for its
ability to handle problems with almost any nonlinearities. However, it is generally difficult to …
ability to handle problems with almost any nonlinearities. However, it is generally difficult to …
Multi-fidelity reduced-order surrogate modelling
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted
computational budget can significantly limit the number of parameter configurations …
computational budget can significantly limit the number of parameter configurations …
Embedding prior knowledge into data-driven structural performance prediction to extrapolate from training domains
Abstract Machine learning (ML)–based data-driven approaches have become increasingly
prevalent for predicting structural performance. Because a properly trained ML model can …
prevalent for predicting structural performance. Because a properly trained ML model can …
[HTML][HTML] A multi-fidelity surrogate model for structural health monitoring exploiting model order reduction and artificial neural networks
Stochastic approaches to structural health monitoring (SHM) are often inevitably limited by
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
In this work we propose an application of physics informed supervised learning strategies to
parametric partial differential equations. Indeed, even if the latter are indisputably useful in …
parametric partial differential equations. Indeed, even if the latter are indisputably useful in …