اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديد$texttt{TRIQS}/texttt{SOM}$: Implementation of the Stochastic Optimization Method for Analytic Continuation
426
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We present the $texttt{TRIQS}/texttt{SOM}$ analytic continuation package, an efficient implementation of the Stochastic Optimization Method proposed by A. Mishchenko et al [Phys. Rev. B $textbf{62}$, 6317 (2000)]. $texttt{TRIQS}/texttt{SOM}$ strives to provide a high quality open source (distributed under the GNU General Public License version 3) alternative to the more widely adopted Maximum Entropy continuation programs. It supports a variety of analytic continuation problems encountered in the field of computational condensed matter physics. Those problems can be formulated in terms of response functions of imaginary time, Matsubara frequencies or in the Legendre polynomial basis representation. The application is based on the $texttt{TRIQS}$ C++/Python framework, which allows for easy interoperability with $texttt{TRIQS}$-based quantum impurity solvers, electronic band structure codes and visualization tools. Similar to other $texttt{TRIQS}$ packages, it comes with a convenient Python interface.
قيم البحث
اقرأ أيضاً
We present $texttt{Maxent}$, a tool for performing analytic continuation of spectral functions using the maximum entropy method. The code operates on discrete imaginary axis datasets (values with uncertainties) and transforms this input to the real a
xis. The code works for imaginary time and Matsubara frequency data and implements the Legendre representation of finite temperature Greens functions. It implements a variety of kernels, default models, and grids for continuing bosonic, fermionic, anomalous, and other data. Our implementation is licensed under GPLv2 and extensively documented. This paper shows the use of the programs in detail.
A method for analytic continuation of imaginary-time correlation functions (here obtained in quantum Monte Carlo simulations) to real-frequency spectral functions is proposed. Stochastically sampling a spectrum parametrized by a large number of delta
-functions, treated as a statistical-mechanics problem, it avoids distortions caused by (as demonstrated here) configurational entropy in previous sampling methods. The key development is the suppression of entropy by constraining the spectral weight to within identifiable optimal bounds and imposing a set number of peaks. As a test case, the dynamic structure factor of the S=1/2 Heisenberg chain is computed. Very good agreement is found with Bethe Ansatz results in the ground state (including a sharp edge) and with exact diagonalization of small systems at elevated temperatures.
Communication is a key bottleneck in distributed training. Recently, an emph{error-compensated} compression technology was particularly designed for the emph{centralized} learning and receives huge successes, by showing significant advantages over st
ate-of-the-art compression based methods in saving the communication cost. Since the emph{decentralized} training has been witnessed to be superior to the traditional emph{centralized} training in the communication restricted scenario, therefore a natural question to ask is how to apply the error-compensated technology to the decentralized learning to further reduce the communication cost. However, a trivial extension of compression based centralized training algorithms does not exist for the decentralized scenario. key difference between centralized and decentralized training makes this extension extremely non-trivial. In this paper, we propose an elegant algorithmic design to employ error-compensated stochastic gradient descent for the decentralized scenario, named $texttt{DeepSqueeze}$. Both the theoretical analysis and the empirical study are provided to show the proposed $texttt{DeepSqueeze}$ algorithm outperforms the existing compression based decentralized learning algorithms. To the best of our knowledge, this is the first time to apply the error-compensated compression to the decentralized learning.
Asteroseismology is well-established in astronomy as the gold standard for determining precise and accurate fundamental stellar properties like masses, radii, and ages. Several tools have been developed for asteroseismic analyses but many of them are
closed-source and therefore not accessible to the general astronomy community. Here we present $texttt{pySYD}$, a Python package for detecting solar-like oscillations and measuring global asteroseismic parameters. $texttt{pySYD}$ was adapted from the IDL-based $texttt{SYD}$ pipeline, which was extensively used to measure asteroseismic parameters for $textit{Kepler}$ stars. $texttt{pySYD}$ was developed using the same well-tested methodology and comes with several new improvements to provide accessible and reproducible results. Well-documented, open-source asteroseismology software that has been benchmarked against closed-source tools are critical to ensure the reproducibility of legacy results from the $textit{Kepler}$ mission. Moreover, $texttt{pySYD}$ will also be a promising tool for the broader astronomy community to analyze current and forthcoming data from the NASA TESS mission.
We present a Fourier Continuation-based parallel pseudospectral method for incompressible fluids in cuboid non-periodic domains. The method produces dispersionless and dissipationless derivatives with fast spectral convergence inside the domain, and
with very high order convergence at the boundaries. Incompressibility is imposed by solving a Poisson equation for the pressure. Being Fourier-based, the method allows for fast computation of spectral transforms. It is compatible with uniform grids (although refined or nested meshes can also be implemented), which in turn allows for explicit time integration at sufficiently high Reynolds numbers. Using a new parallel code named SPECTER we illustrate the method with two problems: channel flow, and plane Rayleigh-Benard convection under the Boussinesq approximation. In both cases the method yields results compatible with previous studies using other high-order numerical methods, with mild requirements on the time step for stability.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
