Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction
A quadratic approximation manifold is presented for performing nonlinear, projection-based,
model order reduction (PMOR). It constitutes a departure from the traditional affine subspace …
model order reduction (PMOR). It constitutes a departure from the traditional affine subspace …
An overview on uncertainty quantification and probabilistic learning on manifolds in multiscale mechanics of materials
C Soize - Mathematics and Mechanics of Complex Systems, 2023 - msp.org
An overview of the author's works, many of which were carried out in collaboration, is
presented. The first part concerns the quantification of uncertainties for complex engineering …
presented. The first part concerns the quantification of uncertainties for complex engineering …
Mesh sampling and weighting for the hyperreduction of nonlinear Petrov–Galerkin reduced‐order models with local reduced‐order bases
The energy‐conserving sampling and weighting (ECSW) method is a hyper‐reduction
method originally developed for accelerating the performance of Galerkin projection‐based …
method originally developed for accelerating the performance of Galerkin projection‐based …
[HTML][HTML] Physics informed and data-based augmented learning in structural health diagnosis
D Di Lorenzo, V Champaney, JY Marzin… - Computer Methods in …, 2023 - Elsevier
Data-based diagnosis has been extensively addressed in the domain of structural health
monitoring. Data exhibit patterns able to infer the existence of damaged areas, and in some …
monitoring. Data exhibit patterns able to infer the existence of damaged areas, and in some …
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
We consider a high-dimensional nonlinear computational model of a dynamical system,
parameterized by a vector-valued control parameter, in the presence of uncertainties …
parameterized by a vector-valued control parameter, in the presence of uncertainties …
Active manifold and model-order reduction to accelerate multidisciplinary analysis and optimization
G Boncoraglio, C Farhat - AIAA Journal, 2021 - arc.aiaa.org
A computational framework is proposed for efficiently solving multidisciplinary analysis and
optimization (MDAO) problems in a relatively high-dimensional design parameter space. It …
optimization (MDAO) problems in a relatively high-dimensional design parameter space. It …
Improved nonlinear analysis of a propeller blade based on hyper-reduction
In this study, an improved nonlinear-analysis framework capable of predicting geometric
nonlinearity and high-speed rotation in rotating structures was developed. A nonlinear time …
nonlinearity and high-speed rotation in rotating structures was developed. A nonlinear time …
Enhanced multimodal nonparametric probabilistic method for model-form uncertainty quantification and digital twinning
The nonparametric probabilistic method (NPM) is a physics-based machine learning
approach for model-form (MF) uncertainty quantification (UQ), model updating, and digital …
approach for model-form (MF) uncertainty quantification (UQ), model updating, and digital …
Concurrent multiscale simulations of nonlinear random materials using probabilistic learning
This work is concerned with the construction of statistical surrogates for concurrent
multiscale modeling in structures comprising nonlinear random materials. The development …
multiscale modeling in structures comprising nonlinear random materials. The development …
Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets
A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it
possible to synthesize solutions to a wide range of nonlinear stochastic boundary value …
possible to synthesize solutions to a wide range of nonlinear stochastic boundary value …