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The Brillouin instability (BI) due to stimulated Brillouin scattering (SBS) and the transverse (thermal) mode instability (TMI) due to stimulated thermal Rayleigh scattering (STRS) limit the achievable power in high-power lasers and amplifiers. The pump power threshold for BI increases as the core diameter increases, but the threshold for TMI may decrease as the core diameter increases. In this paper, we use a multi-time-scale approach to simultaneously model BI and TMI, which gives us the ability to find the fiber diameter with the highest power threshold. We formulate the equations to compare the thresholds of the combined and individual TMI and BI models. At the pump power threshold and below, there is a negligible difference between the full and individual models, as BI and TMI are not strong enough to interact with each other. The highest pump threshold occurs at the optimal core size of 43µm for the simple double-clad geometry that we considered. We found that both effects contribute equally to the threshold, and the full BI and TMI model yields a similar threshold as the BI or TMI model alone. However, once the reflectivity is sufficiently large, we find in the full BI and TMI model that BI may trigger TMI and reduce the TMI threshold to a value lower than is predicted in simulations with TMI alone. This result cannot be predicted by models that consider BI and TMI separately. Our approach can be extended to more complex geometries and used for their optimization.

 

We have investigated the collective electronic and magnetic orderings of a series of La_1−xSr_xMnO₃thin films grown epitaxially strained to (001) oriented strontium titanate substrates as a function of doping,x, for 0 ≤x≤ 0.4. We find that the ground states of these crystalline thin films are, in general, consistent with that observed in bulk crystals and thin film samples synthesized under a multitude of techniques. Our systematic study, however, reveal subtle features in the temperature dependent electronic transport and magnetization measurements, which presumably arise due to Jahn-Teller type distortions in the lattice for particular doping levels. For the parent compound LaMnO₃(x= 0), we report evidence of a strain-induced ferromagnetic ordering in contrast to the antiferromagnetic ground state found in bulk crystals.

 

Abstract Computer model calibration typically operates by fine-tuning parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed for solving multi-objective design problems. Our approach allows us to consider all relevant sources of uncertainty as an integral part of the design process. We demonstrate our proposed approach through both simulation and fine-tuning material design settings to meet performance targets for a wind turbine blade. 

Abstract Calibration of computer models and the use of those design models are two activities traditionally carried out separately. This paper generalizes existing Bayesian inverse analysis approaches for computer model calibration to present a methodology combining calibration and design in a unified Bayesian framework. This provides a computationally efficient means to undertake both tasks while quantifying all relevant sources of uncertainty. Specifically, compared with the traditional approach of design using parameter estimates from previously completed model calibration, this generalized framework inherently includes uncertainty from the calibration process in the design procedure. We demonstrate our approach to the design of a vibration isolation system. We also demonstrate how, when adaptive sampling of the phenomenon of interest is possible, the proposed framework may select new sampling locations using both available real observations and the computer model. This is especially useful when a misspecified model fails to reflect that the calibration parameter is functionally dependent upon the design inputs to be optimized. 

This paper considers the problem of tracking and predicting dynamical processes with model switching. The classical approach to this problem has been to use an interacting multiple model (IMM) which uses multiple Kalman filters and an auxiliary system to estimate the posterior probability of each model given the observations. More recently, data-driven approaches such as recurrent neural networks (RNNs) have been used for tracking and prediction in a variety of settings. An advantage of data-driven approaches like the RNN is that they can be trained to provide good performance even when the underlying dynamic models are unknown. This paper studies the use of temporal convolutional networks (TCNs) in this setting since TCNs are also data-driven but have certain structural advantages over RNNs. Numerical simulations demonstrate that a TCN matches or exceeds the performance of an IMM and other classical tracking methods in two specific settings with model switching: (i) a Gilbert-Elliott burst noise communication channel that switches between two different modes, each modeled as a linear system, and (ii) a maneuvering target tracking scenario where the target switches between a linear constant velocity mode and a nonlinear coordinated turn mode. In particular, the results show that the TCN tends to identify a mode switch as fast or faster than an IMM and that, in some cases, the TCN can perform almost as well as an omniscient Kalman filter with perfect knowledge of the current mode of the dynamical system.