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02/10/2023
GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations
Monte Carlo simulation is widely used to numerically solve stochastic di...
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12/05/2022
AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm
We combine the metrics of distance and isolation to develop the Analytic...
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03/04/2022
Solution of integrals with fractional Brownian motion for different Hurst indices
In this paper, we will evaluate integrals that define the conditional ex...
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01/18/2022
Convergence of a robust deep FBSDE method for stochastic control
In this paper we propose a deep learning based numerical scheme for stro...
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11/24/2021
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications
In the context of solving inverse problems for physics applications with...
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10/11/2021
The One Step Malliavin scheme: new discretization of BSDEs implemented with deep learning regressions
A novel discretization is presented for forward-backward stochastic diff...
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04/03/2021
Monte Carlo Simulation of SDEs using GANs
Generative adversarial networks (GANs) have shown promising results when...
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11/23/2020
Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning
We present a novel reduced order model (ROM) approach for parameterized ...
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09/15/2020
On high-order schemes for tempered fractional partial differential equations
In this paper, we propose third-order semi-discretized schemes in space ...
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09/07/2020
The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations
We propose an accurate data-driven numerical scheme to solve Stochastic ...
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01/31/2020
On Calibration Neural Networks for extracting implied information from American options
Extracting implied information, like volatility and/or dividend, from ob...
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07/05/2019
Exploration of a Cosine Expansion Lattice Scheme
In this article, we combine a lattice sequence from Quasi-Monte Carlo ru...
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04/23/2019
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is propo...
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02/14/2019
Generalisation in fully-connected neural networks for time series forecasting
In this paper we study the generalisation capabilities of fully-connecte...
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01/25/2019
Pricing options and computing implied volatilities using neural networks
This paper proposes a data-driven approach, by means of an Artificial Ne...
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03/14/2017