Importance of experimental methods at research reactors for validation of depletion calculation is discussed. The validation of fuel burnup and nuclide inventory, calculated with stochastic Serpent-2 and hybrid RAPID ...
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作者：
Musyaffa’ AhmadMuhammad Faisala Graduate Program
Department of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Jl. Grafika No. 2 UGM Campus Yogyakarta55281 Indonesia b Master of Mathematics Education Program
Faculty of Mathematics and Natural Sciences Yogyakarta State University Jl. Colombo 1 Yogyakarta55281 Indonesia
A uniform boundary treatment is proposed to construct the exact artificial boundary conditions for the one-dimensional non-local models. To this end, we first convert the governing equation of the non-local model into...
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In this article, we tackle the problem of the existence of a gap corresponding to Young measure relaxations for state-constrained optimal control problems. We provide a counterexample proving that a gap may occur in a...
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Quantum error correction is essential for the development of any scalable quantum computer. In this work we introduce a generalization of a quantum interleaving method for combating clusters of errors in toric quantum...
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We consider the problem of embedding point cloud data sampled from an underlying manifold with an associated flow or velocity. Such data arises in many contexts where static snapshots of dynamic entities are measured,...
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To quantify uncertainties in inverse problems of partial differential equations (PDEs), we formulate them into statistical inference problems using Bayes' formula. Recently, well-justified infinite-dimensional Bay...
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To quantify uncertainties in inverse problems of partial differential equations (PDEs), we formulate them into statistical inference problems using Bayes' formula. Recently, well-justified infinite-dimensional Bayesian analysis methods have been developed to construct dimension-independent algorithms. However, there are three challenges for these infinite-dimensional Bayesian methods: prior measures usually act as regularizers and are not able to incorporate prior information efficiently; complex noises, such as more practical noni.i.d. distributed noises, are rarely considered; and time-consuming forward PDE solvers are needed to estimate posterior statistical quantities. To address these issues, an infinite-dimensional inference framework has been proposed based on the infinite-dimensional variational inference method and deep generative models. Specifically, by introducing some measure equivalence assumptions, we derive the evidence lower bound in the infinite-dimensional setting and provide possible parametric strategies that yield a general inference framework called the Variational Inverting Network (VINet). This inference framework can encode prior and noise information from learning examples. In addition, relying on the power of deep neural networks, the posterior mean and variance can be efficiently and explicitly generated in the inference stage. In numerical experiments, we design specific network structures that yield a computable VINet from the general inference framework. Numerical examples of linear inverse problems of an elliptic equation and the Helmholtz equation are presented to illustrate the effectiveness of the proposed inference framework.
An efficient numerical approach based on weighted average finite differences is used to solve the Newtonian plane Couette flow with wall slip, obeying a dynamic slip law that generalizes the Navier slip law with the i...
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The energy dissipation law and the maximum bound principle are two critical physical properties of the Allen–Cahn equations. While many existing time-stepping methods are known to preserve the energy dissipation law,...
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In view of recently demonstrated joint use of novel Fourier-transform techniques and effective high-accuracy frequency domain solvers related to the Method of Moments, it is argued that a set of trans-formative innova...
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