Zobrazeno 1 - 10
of 541
pro vyhledávání: '"Pardo, David"'
Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a Least Squares solver for the weights of the last layer of the neural network, we improve the con
Externí odkaz:
http://arxiv.org/abs/2407.20417
Whilst the Universal Approximation Theorem guarantees the existence of approximations to Sobolev functions -- the natural function spaces for PDEs -- by Neural Networks (NNs) of sufficient size, low-regularity solutions may lead to poor approximation
Externí odkaz:
http://arxiv.org/abs/2405.14110
We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting
Externí odkaz:
http://arxiv.org/abs/2405.07441
Autor:
Ramirez, Ibai, Pino, Joel, Pardo, David, Sanz, Mikel, del Rio, Luis, Ortiz, Alvaro, Morozovska, Kateryna, Aizpurua, Jose I.
Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal in
Externí odkaz:
http://arxiv.org/abs/2405.06443
Seismic data often contain gaps due to various obstacles in the investigated area and recording instrument failures. Deep learning techniques offer promising solutions for reconstructing missing data parts by leveraging existing information. However,
Externí odkaz:
http://arxiv.org/abs/2404.02632
The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural networks (VPINNs). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on approximatin
Externí odkaz:
http://arxiv.org/abs/2401.04663
We introduce a Robust version of the Variational Physics-Informed Neural Networks method (RVPINNs). As in VPINNs, we define the quadratic loss functional in terms of a Petrov-Galerkin-type variational formulation of the PDE problem: the trial space i
Externí odkaz:
http://arxiv.org/abs/2308.16910
Trace-wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self-supervised deep learning to attenuate this type of noise, the conventional blind-trace deep learning t
Externí odkaz:
http://arxiv.org/abs/2308.10772
Solving PDEs with machine learning techniques has become a popular alternative to conventional methods. In this context, Neural networks (NNs) are among the most commonly used machine learning tools, and in those models, the choice of an appropriate
Externí odkaz:
http://arxiv.org/abs/2305.09578
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and
Externí odkaz:
http://arxiv.org/abs/2304.01802