For Kolmogorov equations associated to nite dimensional stochastic dierential equations (SDEs) in high dimension, a numerical method alternative to Monte Carlo simulations is proposed. The structure of the SDE is inspired by stochastic Partial Dierential Equations (SPDE) and thus contains an underlying Gaussian process which is the key of the algorithm. A series development of the solution in terms of iterated integrals of the Gaussian process is given, it is proved to converge - also in the innite dimensional limit - and it is numerically tested in a number of examples.

For Kolmogorov equations associated to finite dimensional stochastic differential equations (SDEs) in high dimension, a numerical method alternative to Monte Carlo simulations is proposed. The structure of the SDE is inspired by stochastic Partial Differential Equations (SPDE) and thus contains an underlying Gaussian process which is the key of the algorithm. A series development of the solution in terms of iterated integrals of the Gaussian process is given, it is proved to converge - also in the infinite dimensional limit - and it is numerically tested in a number of examples.

### A numerical approach to Kolmogorov equation in high dimension based on Gaussian analysis

#### Abstract

For Kolmogorov equations associated to finite dimensional stochastic differential equations (SDEs) in high dimension, a numerical method alternative to Monte Carlo simulations is proposed. The structure of the SDE is inspired by stochastic Partial Differential Equations (SPDE) and thus contains an underlying Gaussian process which is the key of the algorithm. A series development of the solution in terms of iterated integrals of the Gaussian process is given, it is proved to converge - also in the infinite dimensional limit - and it is numerically tested in a number of examples.
##### Scheda breve Scheda completa Scheda completa (DC)
2021
Settore MAT/06 - Probabilita' e Statistica Matematica
Gaussian process; Iteration schema; Kolmogorov equation; Numerical solution;
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Tipologia: Submitted version (pre-print)
Licenza: Solo Lettura
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/11384/95180`
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