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+34 946 567 842
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+34 946 567 842
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curiarte@bcamath.org
Information of interest
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A Deep Double Ritz Method (D2RM) for solving Partial Differential Equations using Neural Networks
Uriarte, C.
; Pardo, D.; Muga, I.; Muñoz-Matute, J. (2023-02-15)
Residual minimization is a widely used technique for solving Partial Differential Equations in variational form. It minimizes the dual norm of the residual, which naturally yields a saddle-point (min–max) problem over the ...
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Memory-Based Monte Carlo Integration for Solving Partial Differential Equations Using Neural Networks
Uriarte, C.; Taylor, J.M.; Pardo, D.; Rodríguez, O.A.; Vega (2023)
Monte Carlo integration is a widely used quadrature rule to solve Partial Differential Equations with neural networks due to its ability to guarantee overfitting-free solutions and high-dimensional scalability. However, ...
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A Finite Element based Deep Learning solver for parametric PDEs
Uriarte, C.; Pardo, D.; Omella, A. J. (2021)
We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the ...