Search Results for author:
Found 8 papers, 1 papers with code
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs
The model is built on a linear term that can make a reasonably accurate prediction and a nonlinear correction for accuracy improvement.
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
Here, we develop a non-intrusive and data-driven nonlinear reduction approach, exploiting GNNs to encode the reduced manifold and enable fast evaluations of parametrized PDEs.
Multi-fidelity surrogate modeling using long short-term memory networks
Especially for parametrized, time dependent problems in engineering computations, it is often the case that acceptable computational budgets limit the availability of high-fidelity, accurate simulation data.
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks.
Discovery of slow variables in a class of multiscale stochastic systems via neural networks
Finding a reduction of complex, high-dimensional dynamics to its essential, low-dimensional "heart" remains a challenging yet necessary prerequisite for designing efficient numerical approaches.
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
In this work, we present the use of artificial neural networks applied to multi-fidelity regression problems.
Constraint-Aware Neural Networks for Riemann Problems
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates.
Fast prediction and evaluation of gravitational waveforms using surrogate models
Our approach is based on three offline steps resulting in an accurate reduced-order model that can be used as a surrogate for the true/fiducial waveform family.
General Relativity and Quantum Cosmology Computational Engineering, Finance, and Science
