Results 41 to 50 of about 1,899,215 (187)
On Error Estimation for Reduced-order Modeling of Linear Non-parametric and Parametric Systems [PDF]
, 2020 Motivated by a recently proposed error estimator for the transfer function of the reduced-order model of a given linear dynamical system, we further develop more theoretical results in this work.
Benner, Peter, Feng, Lihong
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Generative adversarial reduced order modelling
Scientific ReportsAbstractIn this work, we present GAROM, a new approach for reduced order modeling (ROM) based on generative adversarial networks (GANs). GANs attempt to learn to generate data with the same statistics of the underlying distribution of a dataset, using two neural networks, namely discriminator and generator.
Coscia, Dario +2 more
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Reduced-Order Model Approaches for Predicting Airfoil Performance
ActuatorsThis study delves into the construction of reduced-order models (ROMs) of a flow field over a NACA 0012 airfoil at a moderate Reynolds number and an angle of attack of 8∘. Numerical simulations were computed through the finite-volume solver OpenFOAM. The
Antonio Colanera +3 more
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Advanced Modeling and Simulation in Engineering Sciences, 2022 Most of the methods used today for handling local stress constraints in topology optimization, fail to directly address the non-self-adjointness of the stress-constrained topology optimization problem.
Manyu Xiao +4 more
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Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems
IEEE Access, 2023 This study presents a method for constructing machine learning-based reduced order models (ROMs) that accurately simulate nonlinear contact problems while quantifying epistemic uncertainty.
Teeratorn Kadeethum +4 more
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Reduced Order Modeling with Skew-Radial Basis Functions for Time Series Prediction
Engineering Proceedings, 2023 We present a sparsity-promoting RBF algorithm for time-series prediction. We use a time-delayed embedding framework and model the function from the embedding space to predict the next point in the time series.
Manuchehr Aminian, Michael Kirby
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A DeepONet multi-fidelity approach for residual learning in reduced order modeling
Advanced Modeling and Simulation in Engineering Sciences, 2023 In the present work, we introduce a novel approach to enhance the precision of reduced order models by exploiting a multi-fidelity perspective and DeepONets.
Nicola Demo +2 more
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Reduced form modeling of limit order markets [PDF]
, 2010 This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models by few additional risk factors that describe liquidity properties of the order book ...
Malo, Pekka, Pennanen, Teemu
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Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
, 2017 We discuss a Bayesian formulation to coarse-graining (CG) of PDEs where the coefficients (e.g. material parameters) exhibit random, fine scale variability.
Grigo, Constantin +1 more
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Empirical Reduced-Order Modeling for Boundary Feedback Flow Control
Journal of Control Science and Engineering, 2008 This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. Various aspects of model reduction are discussed that apply to partial differential equation- (PDE-) based models in general.
Seddik M. Djouadi +2 more
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