3,066 research outputs found
The 1-d stochastic wave equation driven by a fractional Brownian motion
In this paper, we develop a Young integration theory in dimension 2 which
will allow us to solve a non-linear one dimensional wave equation driven by an
arbitrary signal whose rectangular increments satisfy some H\"{o}lder
regularity conditions, for some H\"older exponent greater than 1/2. This result
will be applied to the infinite dimensional fractional Brownian motion.Comment: 37 pages, 3 figure
Gaussian estimates for the density of the non-linear stochastic heat equation in any space dimension
In this paper, we establish lower and upper Gaussian bounds for the
probability density of the mild solution to the stochastic heat equation with
multiplicative noise and in any space dimension. The driving perturbation is a
Gaussian noise which is white in time with some spatially homogeneous
covariance. These estimates are obtained using tools of the Malliavin calculus.
The most challenging part is the lower bound, which is obtained by adapting a
general method developed by Kohatsu-Higa to the underlying spatially
homogeneous Gaussian setting. Both lower and upper estimates have the same
form: a Gaussian density with a variance which is equal to that of the mild
solution of the corresponding linear equation with additive noise
Strengthening Model Checking Techniques with Inductive Invariants
This paper describes optimized techniques to efficiently compute and reap benefits from inductive invariants within SAT-based model checking. We address sequential circuit verification, and we consider both equivalences and implications between pairs of nodes in the logic networks. First, we present a very efficient dynamic procedure, based on equivalence classes and incremental SAT, specifically oriented to reduce the set of checked invariants. Then, we show how to effectively integrate the computation of inductive invariants within state-of-the-art SAT-based model checking procedures. Experiments (on more than 600 designs) show the robustness of our approach on verification instances on which stand-alone techniques fai
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