Abstract. Algorithms for system identification, estimation, and adaptive control in stochastic systems rely mostly on different types of signal averaging to achieve uncertainty reduction, convergence, stability, and performance enhancement. The core of such algorithms is various types of laws of large numbers that reduce the effect of noises when they are averaged. Many of the noise sequences e...

We study stochastic transport through a lattice network with quenched disorder and evaluate the limits of predictability of the transport behavior across realizations of spatial heterogeneity. Within a Lagrangian framework, we perform coarse graining, noise averaging, and ensemble averaging, to obtain an effective transport model for the average particle density and its fluctuations between rea...

We address the problem of detecting slowmoving targets using space-time adaptive processing (STAP). The construction of the optimum weights at each range implies the estimation of the clutter covariance matrix. This is typically done by straight averaging of snapshots at neighboring ranges. However, in most configurations, the snapshots’ statistics are range-dependent. Straight averaging thus r...

We present a stochastic evolutionary model obtained through a perturbation of Kauffman’s maximally rugged model, which is recovered as a special case. Our main results are: (i) existence of a percolation-like phase transition in the finite phase space case; (ii) existence of non self-averaging effects in the thermodynamic limit. Lack of self-averaging emerges from a fragmentation of the space o...

We use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant fluid flows. SWAG approximates a posterior distribution of each weight, given training data, and constant learning rate. Having access this distribution, it is able create multiple models various combinations sampled weights, which can be us...

This paper investigates the weighted-averaging dynamic for unconstrained and constrained consensus problems. Through the use of a suitably defined adjoint dynamic, quadratic Lyapunov comparison functions are constructed to analyze the behavior of weighted-averaging dynamic. As a result, new convergence rate results are obtained that capture the graph structure in a novel way. In particular, the...