Results 201 to 210 of about 8,322,548 (230)
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1992
Before about 1956, there was no systematic statistical theory of nuclear energy level structure. There was a shortage of close spacings in experimentally obtained energy levels which was generally dismissed as being due to instrumental resolution failings.
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Before about 1956, there was no systematic statistical theory of nuclear energy level structure. There was a shortage of close spacings in experimentally obtained energy levels which was generally dismissed as being due to instrumental resolution failings.
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Random matrix theory and the scaling theory of localization
Physical Review Letters, 1990We consider the most probable value of conductance of a disordered quantum conductor in the framework of the random matrix theory developed earlier. Analytic calculations are possible in the metallic as well as strongly localized regimes. We make a simple assumption on the eigenvalue density, as suggested by numerical work, and explore the consequences
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On Random Matrix Theory for stationary processes
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010Random Matrix Theory has generated tremendous interest in recent years, partly from powerful results developed for multi-user detection theory but also for growing applications in statistics, signal processing and econometrics. However the current theory has emphasized white noise data.
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2004
In this chapter, we will work not with \(\mathrm{GL}(n, \mathbb{C})\) but with its compact subgroup U(n). As in the previous chapters, we will consider elements of \(\mathcal{R}_{k}\) as generalized characters on S k . If \(\mathbf{f} \in \mathcal{R}_{k}\), then \(f ={ \mathrm{ch}}^{(n)}(\mathbf{f}) \in \varLambda _{k}^{(n)}\) is a symmetric polynomial
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In this chapter, we will work not with \(\mathrm{GL}(n, \mathbb{C})\) but with its compact subgroup U(n). As in the previous chapters, we will consider elements of \(\mathcal{R}_{k}\) as generalized characters on S k . If \(\mathbf{f} \in \mathcal{R}_{k}\), then \(f ={ \mathrm{ch}}^{(n)}(\mathbf{f}) \in \varLambda _{k}^{(n)}\) is a symmetric polynomial
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On Random Matrix Theory and Autoregressive Modeling
2019 IEEE 58th Conference on Decision and Control (CDC), 2019Random matrix theory has attracted growing interest in signal processing and communications over the last one or two decades. It has gained further impetus due to the upsurge in the occurrence of ’big data’. However so far little of this interest has seeped into system identification.
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Test of the Eigenstate Thermalization Hypothesis Based on Local Random Matrix Theory
Physical Review Letters, 2021Masahito Ueda +2 more
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Random matrix theory for robust topology optimization with material uncertainty
Structural and Multidisciplinary Optimization, 2023Linxi Li, Craig Steeves
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