An RG-based reduction produces a local Hopf amplitude equation and slow-manifold reconstruction for nonlinear aeroelastic systems with polynomial nonlinearities.
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Interpolation-based ROM techniques with Q-DEIM hyper-reduction are applied to reduce computational cost and memory use of stochastic integrals in the SFV method for high-dimensional stochastic spaces.
A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
A POD-RBF reduced-order model predicts 3D unsteady flow around cylinders with periodic inlet velocity changes, cutting CPU time by over 99% while keeping accuracy loss below 5.2%.
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RG-Based Local Hopf Reduction and Slow-Manifold Reconstruction for Nonlinear Aeroelastic Systems
An RG-based reduction produces a local Hopf amplitude equation and slow-manifold reconstruction for nonlinear aeroelastic systems with polynomial nonlinearities.
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Model Order Reduction Techniques for the Stochastic Finite Volume Method
Interpolation-based ROM techniques with Q-DEIM hyper-reduction are applied to reduce computational cost and memory use of stochastic integrals in the SFV method for high-dimensional stochastic spaces.
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A probabilistic reduced-order modeling framework for patient-specific cardio-mechanical analysis
A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
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Reduced-order modelling of parametrized unsteady Navier-Stokes equations and application to flow around cylinders with periodic changing boundary conditions
A POD-RBF reduced-order model predicts 3D unsteady flow around cylinders with periodic inlet velocity changes, cutting CPU time by over 99% while keeping accuracy loss below 5.2%.