Now showing items 1-9 of 9

    • Quasi-Regression Monte-Carlo Method for Semi-Linear PDEs and BSDEs 

      Gobet, Emmanuel; López-Salas, José Germán; Vázquez, Carlos (MDPI AG, 2019-08-06)
      [Abstract] In this work we design a novel and efficient quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations (BSDEs), and we analyze the ...
    • A New Technique for Improved Use of Thermal Energy from Waste Effluents 

      Magide-Ameijide, José Manuel; Varela Rodríguez, Hiram; López-Fabal, Adolfo (MDPI AG, 2020-01-09)
      [Abstract] Energy sustainability and environmental protection in general are at the heart of engineering and industry discussions. Countless efforts have been devoted to improving the energy efficiency of industrial processes ...
    • Computation of Resonance Modes in Open Cavities with Perfectly Matched Layers 

      Hervella-Nieto, Luis María; Prieto, A.; Recondo, Sara (MDPI AG, 2020-08-18)
      [Abstract] During the last decade, several authors have addressed that the Perfectly Matched Layers (PML) technique can be used not only for the computation of the near-field in time-dependent and time-harmonic scattering ...
    • European and American Options Valuation by Unsupervised Learning with Artificial Neural Networks 

      Salvador, Beatriz; Oosterlee, Cornelis W.; Meer, Remco van der (MDPI AG, 2020-08-19)
      [Abstract] Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and American financial options, based ...
    • Numerical Simulation of a Nonlinear Problem Arising in Heat Transfer and Magnetostatics 

      González Taboada, María; Varela Rodríguez, Hiram (MDPI AG, 2020-08-19)
      [Abstract] We present a numerical model that comprises a nonlinear partial differential equation. We apply an adaptive stabilised mixed finite element method based on an a posteriori error indicator derived for this ...
    • Machine Learning to Compute Implied Volatility from European/American Options Considering Dividend Yield 

      Liu, Shuaiqiang; Leitao, Álvaro; Borovykh, Anastasia; Oosterlee, Cornelis (MDPI AG, 2020-09-15)
      [Abstract] Computing implied volatility from observed option prices is a frequent and challenging task in finance, even more in the presence of dividends. In this work, we employ a data-driven machine learning approach ...
    • Deep Learning-Based Method for Computing Initial Margin † 

      Pérez Villarino, Joel; Leitao, Álvaro (MDPI, 2021)
      [Abstract] Following the guidelines of the Basel III agreement (2013), large financial institutions are forced to incorporate additional collateral, known as Initial Margin, in their transactions in OTC markets. Currently, ...
    • On the Adaptive Numerical Solution to the Darcy–Forchheimer Model † 

      González Taboada, María; Varela Rodríguez, Hiram (MDPI, 2021)
      [Abstract] We considered a primal-mixed method for the Darcy–Forchheimer boundary value problem. This model arises in fluid mechanics through porous media at high velocities. We developed an a posteriori error analysis of ...
    • Quantum Arithmetic for Directly Embedded Arrays 

      Manzano, Alberto; Musso, Daniele; Leitao, Álvaro; Gómez, Andrés; Vázquez, Carlos; Ordóñez, Gustavo; Rodríguez Nogueiras, María (MDPI, 2021)
      [Abstract] We describe a general-purpose framework to implement quantum algorithms relying upon an efficient handling of arrays. The cornerstone of the framework is the direct embedding of information into quantum amplitudes, ...