Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models

We consider the smoothing problem for a class of conditionally linear Gaussian state-space (CLGSS) models, referred to as mixed linear/nonlinear models. In contrast to the better studied hierarchical CLGSS models, these allow for an intricate cross dependence between the linear and the nonlinear parts of the state vector. We derive a Rao-Blackwellized particle smoother (RBPS) for this model class by exploiting its tractable substructure. The smoother is of the forward filtering/backward simulation type. A key feature of the proposed method is that, unlike existing RBPS for this model class, the linear part of the state vector is marginalized out in both the forward direction and in the backward direction. © 2013 IEEE.

Nonlinear optics in Silicon photonic crystal cavities

Silicon is known to be a very good material for the realization of high-Q, low-volume photonic cavities, but at the same it is usually considered as a poor material for nonlinear optical functionalities like second-harmonic generation, because its second-order nonlinear susceptibility vanishes in the dipole approximation. In this work we demonstrate that nonlinear optical effects in silicon nanocavities can be strongly enhanced and even become macroscopically observable. We employ photonic crystal nanocavities in silicon membranes that are optimized simultaneously for high quality factor and efficient coupling to an incoming beam in the far field. Using a low-power, continuous-wave laser at telecommunication wavelengths as a pump beam, we demonstrate simultaneous generation of second- and third harmonics in the visible region, which can be observed with a simple camera. The results are in good agreement with a theoretical model that treats third-harmonic generation as a bulk effect in the cavity region, and second-harmonic generation as a surface effect arising from the vertical hole sidewalls. Optical bistability is also observed in the silicon nanocavities and its physical mechanisms (optical, due to two-photon generation of free carriers, as well as thermal) are investigated. © 2011 IEEE.

Dynamic learning with the EM algorithm for neural networks

In this paper, we derive an EM algorithm for nonlinear state space models. We use it to estimate jointly the neural network weights, the model uncertainty and the noise in the data. In the E-step we apply a forwardbackward Rauch-Tung-Striebel smoother to compute the network weights. For the M-step, we derive expressions to compute the model uncertainty and the measurement noise. We find that the method is intrinsically very powerful, simple and stable.

Ultrafast and high-energy pulsed lasers with graphene mode-lockers

The conventional technology for generating ultrashort pulses relies on soliton-like operation based mode-locking. In this regime, the pulse duration is limited by nonlinear optical effects[1]. One method to mitigate these effects is to alternate segments of normal and anomalous group velocity dispersion (GVD) fiber[1]. This configuration is known as dispersion-managed soliton design. It decreases the nonlinear optical effects and reduces the pulse duration[1]. © 2011 IEEE.

State-space inference and learning with Gaussian processes

State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. Copyright 2010 by the authors.

Mixed nonlinear LES for DES suitable flows

It is shown that a new mixed nonlinear/eddy viscosity LES model reproduces profiles better than a number of competing nonlinear and mixed models for plane channel flow. The objective is an LES method that produces a fully resolved turbulent boundary layer and could be applied to a variety of aerospace problems that are currently studied with RANS, RANS-LES, or DES methods that lack a true turbulent boundary layer. There are two components to the new model. One an eddy viscosity based upon the advected subgrid scale energy and a relatively small coefficient. Second, filtered nonlinear terms based upon the Leray regularization. Coefficients for the eddy viscosity and nonlinear terms come from LES tests in decaying, isotropic turbulence. Using these coefficients, the velocity profile matches measurements data at Reτ ≈ 1000 exactly. Profiles of the components of kinetic energy have the same shape as in the experiment, but the magnitudes differ by about 25%. None of the competing LES gets the shape correct. This method does not require extra operations at the transition between the boundary layer and the interior flow.