000916214 001__ 916214
000916214 005__ 20221221131716.0
000916214 0247_ $$2arXiv$$aarXiv:2212.02365
000916214 0247_ $$2Handle$$a2128/33235
000916214 037__ $$aFZJ-2022-06015
000916214 1001_ $$0P:(DE-Juel1)188816$$aKim, Jangho$$b0$$eCorresponding author$$ufzj
000916214 1112_ $$aThe 39th International Symposium on Lattice Field Theory, LATTICE2022$$cBonn$$d2022-08-08 - 2022-08-13$$wGermany
000916214 245__ $$aError reduction using machine learning on Ising worm simulation
000916214 260__ $$c2022
000916214 300__ $$a018
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000916214 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1671621761_4311
000916214 500__ $$a8 pages, 18 figures, 39th International Symposium on Lattice Field Theory, LATTICE2022 8th-13th August 2022, Bonn, Germany
000916214 520__ $$aWe develop a method to improve on the statistical errors for higher moments using machine learning techniques. We present here results for the dual representation of the Ising model with an external field, derived via the high temperature expansion and simulated by the worm algorithm. We compare two ways of measuring the same set of observables, without and with machine learning: moments of the magnetization and the susceptibility can be improved by using the decision tree method to train the correlations between the higher moments and the second moment obtained from an integrated 2-point function. Those results are compared in small volumes to analytic predictions.
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000916214 588__ $$aDataset connected to arXivarXiv
000916214 7001_ $$0P:(DE-HGF)0$$aUnger, Wolfgang$$b1
000916214 8564_ $$uhttps://juser.fz-juelich.de/record/916214/files/2212.02365.pdf$$yOpenAccess
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000916214 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188816$$aForschungszentrum Jülich$$b0$$kFZJ
000916214 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a bielefeld university$$b1
000916214 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
000916214 9141_ $$y2022
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000916214 9201_ $$0I:(DE-Juel1)IAS-4-20090406$$kIAS-4$$lTheorie der Starken Wechselwirkung$$x0
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