Pages

Other

Smaller generalization error derived for deep compared to shallow residual neural networks

Bibliography: A. Kammonen and J. Kiessling and P. Plechac and M. Sandberg and A. Szepessy and R. Tempone. "Smaller generalization error derived for deep compared to shallow residual neural networks", arXiv preprint arXiv:2010.01887, September 2020.​ Authors: A. Kammonen And J. Kiessling And P. Plechac And M. Sandberg And A. Szepessy And And R. Tempone Keywords: residual network, deep random feature, supervised learning, error estimates, layer by layer algorithm. Year: 2020 Website

Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability

Bibliography: ​Litvinenko, Alexander, Dimitry Logashenko, Raúl Tempone, Gabriel Wittum, and David Keyes. . "Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability". Int J Geomath 11, 10 (2020) Authors: Alexander Litvinenko, Dimitry Logashenko, Raúl Tempone, Gabriel Wittum, David Keyes Keywords: Uncertainty quantification·ug4·Multigrid·Density-driven flow· Reservoir·Groundwater·Salt formations Year: 2020 Abstract: ​The pollution of groundwater, essential for supporting populations and agriculture, can have catastrophic consequences. Thus, accurate

Weak error rates for option pricing under the rough Bergomi model

Bibliography: Christian Bayer and Eric Joseph Hall and Raúl Tempone​. "Weak error rates for ​option pricing under the rough Bergomi model.​​" arXiv preprint arXiv:2009.01219​​​ Authors: Christian Bayer And Eric Joseph Hall And Raúl Tempone​. Keywords: Rough Bergomi, Weak error rate Year: 2020 Abstract: In quantitative finance, modeling the volatility structure of underlying assets is a key component in the pricing of options. Rough stochastic volatility models, such as the rough Bergomi model [Bayer, Friz, Gatheral, Quantitative Finance 16(6), 887-904, 2016], seek to fit observed market data