Machine learning regression techniques for the modeling of complex systems: An overview
R Trinchero, F Canavero - IEEE Electromagnetic Compatibility …, 2021 - ieeexplore.ieee.org
Recently, machine learning (ML) techniques have gained widespread diffusion, since they
have been successfully applied in several research fields. This paper investigates the …
have been successfully applied in several research fields. This paper investigates the …
Assessment of DeepONet for time dependent reliability analysis of dynamical systems subjected to stochastic loading
S Garg, H Gupta, S Chakraborty - Engineering Structures, 2022 - Elsevier
Time dependent reliability analysis and uncertainty quantification of structural system
subjected to stochastic forcing function is a challenging endeavour as it necessitates …
subjected to stochastic forcing function is a challenging endeavour as it necessitates …
Surrogate models for oscillatory systems using sparse polynomial chaos expansions and stochastic time warping
Polynomial chaos expansions (PCEs) have proven efficiency in a number of fields for
propagating parametric uncertainties through computational models of complex systems …
propagating parametric uncertainties through computational models of complex systems …
Compressed machine learning-based inverse model for design optimization of microwave components
This article presents a new noniterative inverse modeling technique based on machine
learning regression and its applications to microwave design optimization. The proposed …
learning regression and its applications to microwave design optimization. The proposed …
A probabilistic machine learning approach for the uncertainty quantification of electronic circuits based on gaussian process regression
P Manfredi, R Trinchero - IEEE Transactions on Computer …, 2021 - ieeexplore.ieee.org
This article introduces a probabilistic machine learning framework for the uncertainty
quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR) …
quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR) …
A data compression strategy for the efficient uncertainty quantification of time-domain circuit responses
P Manfredi, R Trinchero - IEEE Access, 2020 - ieeexplore.ieee.org
This paper presents an innovative modeling strategy for the construction of efficient and
compact surrogate models for the uncertainty quantification of time-domain responses of …
compact surrogate models for the uncertainty quantification of time-domain responses of …
Reduced-order modeling via proper generalized decomposition for uncertainty quantification of frequency response functions
This paper presents a new reduced-order modeling methodology for frequency response
analysis of linear dynamical systems with parametric uncertainty. The proposed framework …
analysis of linear dynamical systems with parametric uncertainty. The proposed framework …
Uncertainty analysis of mechanical dynamics by combining response surface method with signal decomposition technique
Studying multibody dynamic systems, a common way to evaluate the effects of uncertainty
parameters is the response surface method, which works by building a polynomial surrogate …
parameters is the response surface method, which works by building a polynomial surrogate …
Uncertainty propagation of frequency response functions using a multi-output Gaussian Process model
Uncertainty propagation of frequency response functions (FRFs) under parameter variations
is crucial for structural design and reliability analysis. However, obtaining sufficiently large …
is crucial for structural design and reliability analysis. However, obtaining sufficiently large …
Performance variability analysis of photonic circuits with many correlated parameters
We propose a method to analyze the performance variability caused by fabrication
uncertainty in photonic circuits with a large number of correlated parameters. By combining …
uncertainty in photonic circuits with a large number of correlated parameters. By combining …