THE PHOTON-LIKE FLYING QUBIT IN THE COUPLED CAVITY ARRAY
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We propose the simulation for an effective scheme to realize a spin network with tunable long-range couplings in the coupled cavity array with external multi-driving lasers. Via this scheme, the linear photon-like dispersion relation is achievable, which could be employed to perform a perfect quantum state transfer. Numerical results show that when applying two lasers in each cavity, the fidelity is higher than the highest fidelity of a classical transfer even for the transfer distance l increases up to 100 sites. In the simulation, as the number of lasers increases, the fidelity will be evidently enhanced for a wide range of l.Keywords:
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Since its introduction the fidelity concept has been used to evaluate the time behavior of UWB antennas. However, fidelity has been employed with different meanings. This paper clarifies the differences between fidelity factor, system fidelity factor, and fidelity factor of the system. A recently developed UWB antenna has been taken as a representative one to illustrate the differences among these concepts.
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E-03 VECTOR FIDELITY ANALYSES OF SEABED SEISMIC DATA GEIR WOJE EIVIND BERG JON IVAR RYKKELID ØYSTEIN SVENDSEN Abstract 1 The vector fidelity analyses are performed on the first break of three different data sets. RMS mapping modelling polarization analyses and frequency analyses all prove to differentiate the sensors with respect to vector fidelity. One cable sensor and three planted sensors (nodes) are evaluated. There are significant differences between the cable sensor and the planted nodes. The results suggest that cable sensors are not qualified for wide azimuth acquisitions. Introduction During the last years vector fidelity has become a major subject
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Operator learning for complex nonlinear systems is increasingly common in modeling multi-physics and multi-scale systems. However, training such high-dimensional operators requires a large amount of expensive, high-fidelity data, either from experiments or simulations. In this work, we present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets. We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.
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Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computationally demanding, we investigate the use of simulators with different levels of fidelity. As a first step, we express the overall safety specification in terms of environmental parameters and structure this safety specification as an optimization problem. We propose a multi-fidelity falsification framework using Bayesian optimization, which is able to determine at which level of fidelity we should conduct a safety evaluation in addition to finding possible instances from the environment that cause the system to fail. This method allows us to automatically switch between inexpensive, inaccurate information from a low-fidelity simulator and expensive, accurate information from a high-fidelity simulator in a cost-effective way. Our experiments on various environments in simulation demonstrate that multi-fidelity Bayesian optimization has falsification performance comparable to single-fidelity Bayesian optimization but with much lower cost.
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The conceptual and preliminary design stages of aircraft design have traditionally been two separate, time intensive design phases. However, the concept of dialable fidelity is one in which the gap between these two phases, conceptual and preliminary design, is bridged by introducing physics into the design process at an earlier stage than traditionally employed. This introduction of physics at an earlier stage eliminates the need for separate conceptual and preliminary design phases and consequently reduces total design time. The concept of dialable fidelity does however pose certain obstacles such as determining how and when to dial or switch between different fidelity models. This paper explores the ability to apply an adjustment factor to low fidelities models so as to scale them to high fidelity models. A response surface approximation of said adjustment factor is constructed using information of previous high and low fidelity simulations at corresponding design points using kriging. In doing so, finite difference estimations for sensitivities of a high fidelity model can be obtained using low fidelity models. It is shown that the error between predictions of a high fidelity model and low fidelity model decreases by applying an adjustment factor to the low fidelity model. Likewise, the error between the high fidelity and adjusted low fidelity model tends to decrease as more data is available for construction of the kriging model used to determine the adjustment factor.
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Developers of medical simulators and instructors who use them often have questions about the level of fidelity needed in a simulation. In this article, we address the nature of fidelity with respect to virtual reality training systems. We argue that high-fidelity simulators do not always lead to better performance, and in some instances, can interfere with performance. The primary reason for these seemingly counterintuitive findings lies with a fundamental understanding of how humans perceive and process sensory information. Consequently, simulation-based training systems should be developed to maximize their effectiveness, not their fidelity.
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Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements. Because full-fidelity simulations can be computationally demanding, we investigate the use of simulators with different levels of fidelity. As a first step, we express the overall safety specification in terms of environment parameters and structure this safety specification as an optimization problem. We propose a multi-fidelity falsification framework using Bayesian optimization, which is able to determine at which level of fidelity we should conduct a safety evaluation in addition to finding possible instances from the environment that cause the system to fail. This method allows us to automatically switch between inexpensive, inaccurate information from a low-fidelity simulator and expensive, accurate information from a high-fidelity simulator in a cost-effective way. Our experiments on various environments in simulation demonstrate that multi-fidelity Bayesian optimization has falsification performance comparable to single-fidelity Bayesian optimization but with much lower cost.
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The main idea behind multi-fidelity methods is to utilize cheaper, lower-fidelity models – than the intended high-fidelity, expensive model of the problem – to generate a baseline solution that together with relatively small number of high-fidelity simulations can lead to accurate predictions. The methods are briefly presented, and their performance assessed on an irradiated particle-laden turbulent flow case related to Stanford’s PSAAP II particle-based solar energy receiver.
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High-fidelity models are capable of providing accurate estimates but slow in execution. On the other hand, estimates provided by low-fidelity models are biased but fast. The knowledge embedded in low-fidelity models might be helpful for simulation optimization algorithms. Several multi-fidelity modeling algorithms have been proposed in literature, whereas currently only high-fidelity information is used in the initial sampling phase. This poster provides an algorithm to allocate high-fidelity budgets using multi-fidelity information in order to contain a fixed number of good solutions in the initial design. Results show that the proposed sampling policy can allocate more budgets in promising areas.
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