We discuss analytically and numerically the propagation and energy transmission of electromagnetic waves caused by the coupling of surface plasmon polaritons (SPPs) between two spatially separated layers of 2D materials, such as graphene, at subwavelength distances. We construct an adaptive finite-element method to compute the ratio of energy transmitted within these waveguide structures reliably and efficiently. At its heart, the method is built upon a goaloriented a posteriori error estimation with the dual-weighted residual method (DWR).Furthermore, we derive analytic solutions of the two-layer system, compare those to (known) single-layer configurations, and compare and validate our numerical findings by comparing numerical and analytical values for optimal spacing of the two-layer configuration. Additional aspects of our numerical treatment, such as local grid refinement, and the utilization of perfectly matched layers (PMLs) are examined in detail.
VPIN (Volume synchronized Probability of Informed trading) is a leading indicator of liquidity-induced volatility. It is best known for having produced a signal more than hours before the Flash Crash of 2010. On that day, the market saw the biggest one-day point decline in the Dow Jones Industrial Average, which culminated to the market value of $1 trillion disappearing, but only to recover those losses twenty minutes later (Lauricella 2010).The computation of VPIN requires the user to set up a handful of free parameters. The values of these parameters significantly affect the effectiveness of VPIN as measured by the false positive rate (FPR). An earlier publication reported that a brute-force search of simple parameter combinations yielded a number of parameter combinations with FPR of 7%. This work is a systematic attempt to find an optimal parameter set using an optimization package, NOMAD (Nonlinear Optimization by Mesh Adaptive Direct Search) by Audet, le digabel, and tribes (2009) and le digabel (2011). We have implemented a number of techniques to reduce the computation time with NOMAD. Tests show that we can reduce the FPR to only 2%.To better understand the parameter choices, we have conducted a series of sensitivity analysis via uncertainty quantification on the parameter spaces using UQTK (Uncertainty Quantification Toolkit). Results have shown dominance of 2 parameters in the computation of FPR. Using the outputs from NOMAD optimization and sensitivity analysis, We recommend A range of values for each of the free parameters that perform well on a large set of futures trading records.
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