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08/07/2023
Positional Embeddings for Solving PDEs with Evolutional Deep Neural Networks
This work extends the paradigm of evolutional deep neural networks (EDNN...
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05/16/2023
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs
Accurate prediction of aerodynamic forces in real-time is crucial for au...
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05/15/2023
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
The present work proposes a framework for nonlinear model order reductio...
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03/06/2023
Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems
Reduced-order models are indispensable for multi-query or real-time prob...
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10/30/2022
A new variable shape parameter strategy for RBF approximation using neural networks
The choice of the shape parameter highly effects the behaviour of radial...
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08/05/2022
Multi-fidelity surrogate modeling using long short-term memory networks
When evaluating quantities of interest that depend on the solutions to d...
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01/14/2022
Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equation
High-resolution simulations of particle-based kinetic plasma models typi...
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11/02/2021
FC-based shock-dynamics solver with neural-network localized artificial-viscosity assignment
This paper presents a spectral scheme for the numerical solution of nonl...
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09/22/2021
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
This work deals with the investigation of bifurcating fluid phenomena us...
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05/17/2021
A hierarchical preconditioner for wave problems in quasilinear complexity
The paper introduces a novel, hierarchical preconditioner based on neste...
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04/28/2021
Discovery of slow variables in a class of multiscale stochastic systems via neural networks
Finding a reduction of complex, high-dimensional dynamics to its essenti...
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03/22/2021
Efficient numerical room acoustic simulations with parametrized boundaries using the spectral element and reduced basis method
Numerical methods can be used to simulate wave propagation in rooms, wit...
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02/26/2021
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Highly accurate numerical or physical experiments are often time-consumi...
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07/26/2020
Rank-adaptive structure-preserving reduced basis methods for Hamiltonian systems
This work proposes an adaptive structure-preserving model order reductio...
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04/29/2019