A Review of Recent Advances in Surrogate Models for Uncertainty Quantification of High-Dimensional Engineering Applications
Z Azarhoosh, MI Ghazaan - Computer Methods in Applied Mechanics and …, 2025 - Elsevier
In fields where predictions may have vital consequences, uncertainty quantification (UQ)
plays a crucial role, as it enables more accurate forecasts and mitigates the potential risks …
plays a crucial role, as it enables more accurate forecasts and mitigates the potential risks …
[HTML][HTML] Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Highly accurate simulations of complex phenomena governed by partial differential
equations (PDEs) typically require intrusive methods and entail expensive computational …
equations (PDEs) typically require intrusive methods and entail expensive computational …
Scientific machine learning for closure models in multiscale problems: A review
Closure problems are omnipresent when simulating multiscale systems, where some
quantities and processes cannot be fully prescribed despite their effects on the simulation's …
quantities and processes cannot be fully prescribed despite their effects on the simulation's …
Explainable causal variational autoencoders based equivariant graph neural networks for analyzing the consumer purchase behavior in E-commerce
As the usage of e-commerce is growing rapidly, it is significant to analyze the features or
attributes that influence the consumer purchase behavior in E-commerce. This study …
attributes that influence the consumer purchase behavior in E-commerce. This study …
Active learning inspired multi-fidelity probabilistic modelling of geomaterial property
The identification of geomaterial properties is costly but pivotal for engineering design. A
wide range of approaches perform well with sufficiently measured data but their …
wide range of approaches perform well with sufficiently measured data but their …
Operator inference driven data assimilation for high fidelity neutron transport
This paper presents a novel reduced-order model (ROM) based data assimilation framework
for parametric high-fidelity time-dependent neutron transport equations (TNTE). The ROM is …
for parametric high-fidelity time-dependent neutron transport equations (TNTE). The ROM is …
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 …
[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 unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator
This study presents a novel unsupervised convolutional Neural Network (NN) architecture
with nonlocal interactions for solving Partial Differential Equations (PDEs). The nonlocal …
with nonlocal interactions for solving Partial Differential Equations (PDEs). The nonlocal …
[HTML][HTML] Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation,
entail a huge computational complexity when dealing with input-output maps involving the …
entail a huge computational complexity when dealing with input-output maps involving the …