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Lukas Anzeletti

Publications and source records attributed to Lukas Anzeletti.

7 recordsLinked to original sources

Overcoming the spatial order barrier for nonlinear SPDEs with additive space-time white noise

We introduce a fully discrete numerical scheme for semilinear SPDEs with additive space-time white noise that overcomes the previous order barrier for the spatial convergence rate. The scheme achieves a strong convergence rate of $M^{-1+ε}$ in time and $N^{-3/2+ε}$ in space for any $ε>0$, where $M^{-1}$ and $N^{-1}$ are the temporal, respectively the spatial, meshsizes. This substantially improves the standard spatial error bounds of order $N^{-1/2}$ in the literature.

math.NA↗

Uniqueness for stochastic differential equations in Hilbert spaces with irregular drift

We present a versatile framework to study strong existence and uniqueness for stochastic differential equations (SDEs) in Hilbert spaces with irregular drift. We consider an SDE in a separable Hilbert space $H$ \begin{equation*} dX_t= (A X_t + b(X_t))dt +(-A)^{-γ/2}dW_t,\quad X_0=x_0 \in H, \end{equation*} where $A$ is a self-adjoint negative definite operator with purely atomic spectrum, $W$ is a cylindrical Wiener process, $b$ is $α$-Hölder continuous function $H\to H$, and a nonnegative parameter $γ$ such that the stochastic convolution takes values in $H$. We show that this equation has a unique strong solution provided that $α> α^*(γ)$, with an explicit function $α^*$ that takes values in $(0,1)$ for all $γ\in[0,3)$. This substantially extends the seminal work of Da Prato and Flandoli (2010) as no structural assumption on $b$ is imposed. The range of admissible $α$ is also extended. To obtain this result, we do not use infinite-dimensional Kolmogorov equations but instead develop a new technique combining Lê's theory of stochastic sewing in Hilbert spaces, Gaussian analysis, and a method of Lasry and Lions for approximation in Hilbert spaces.

math.PR↗

Path-by-path uniqueness for stochastic differential equations under Krylov-Röckner condition

We show that any stochastic differential equation (SDE) driven by Brownian motion with drift satisfying the Krylov-Röckner condition has exactly one solution in an ordinary sense for almost every trajectory of the Brownian motion. Consequentially, such SDE is strongly complete and forms a random dynamical system. Also, a further application to a boundary value problem is discussed.

math.PR↗

On the density of singular SDEs with fractional noise and applications to McKean-Vlasov equations

We investigate properties of the (conditional) law of the solution to SDEs driven by fractional Brownian noise with a singular, possibly distributional, drift. Our results on the law are twofold: i) we quantify the spatial regularity of the law, while keeping track of integrability in time, and ii) we prove that it has a density with Gaussian tails. Then the former result is used to establish novel results on existence and uniqueness of solutions to McKean-Vlasov equations of convolutional type.

math.PR↗

A note on weak existence for SDEs driven by fractional Brownian motion

We are interested in existence of solutions to the $d$-dimensional equation \begin{equation*} X_t=x_0+\int_0^t b(X_s)ds + B_t, \end{equation*} where $B$ is a (fractional) Brownian motion with Hurst parameter $H\leqslant 1/2$ and $b$ is an $\mathbb{R}^d$-valued measure in some Besov space. We exhibit a class of drifts $b$ such that weak existence holds. In particular existence of a weak solution is shown for $b$ being a finite $\mathbb{R}^d$-valued measure for any $H<1/(2d)$.

math.PR↗

Regularisation by fractional noise for one-dimensional differential equations with distributional drift

We study existence and uniqueness of solutions to the equation $dX_t=b(X_t)dt + dB_t$, where $b$ is a distribution in some Besov space and $B$ is a fractional Brownian motion with Hurst parameter $H\leqslant 1/2$. First, the equation is understood as a nonlinear Young equation. This involves a nonlinear Young integral constructed in the space of functions with finite $p$-variation, which is well suited when $b$ is a measure. Depending on $H$, a condition on the Besov regularity of $b$ is given so that solutions to the equation exist. The construction is deterministic, and $B$ can be replaced by a deterministic path $w$ with a sufficiently smooth local time. Using this construction we prove the existence of weak solutions (in the probabilistic sense). We also prove that solutions coincide with limits of strong solutions obtained by regularisation of $b$. This is used to establish pathwise uniqueness and existence of a strong solution. In particular when $b$ is a finite measure, weak solutions exist for $H<\sqrt{2}-1$, while pathwise uniqueness and strong existence hold when $H\leqslant 1/4$. The proofs involve fine properties of the local time of the fractional Brownian motion, as well as new regularising properties of this process which are established using the stochastic sewing Lemma.

math.PR↗

Comparison of classical and path-by-path solutions to SDEs

We consider the Stochastic Differential Equation $X_t = X_0 + \int_0^t b(s,X_s) ds + B_t$, in $\mathbb{R}^d$. We give an example of a drift $b$ such that there does not exist a weak solution, but there exists a solution for almost every realization of the Brownian motion $B$. We also give an explicit example of a drift such that the SDE has a pathwise unique weak solution, but path-by-path uniqueness (i.e. uniqueness of solutions to the ODE for almost every realization of the Brownian motion) is lost. These counterexamples extend the results obtained in arXiv:2001.02869 to dimension $d=1$.

math.PR↗