000905641 001__ 905641
000905641 005__ 20220131120338.0
000905641 037__ $$aFZJ-2022-00868
000905641 1001_ $$0P:(DE-HGF)0$$aEffenberger, Frederic$$b0$$eCorresponding author
000905641 1112_ $$aAGU Fall Meeting$$cNew Orleans$$d2021-12-13 - 2021-12-17$$gAGU$$wUSA
000905641 245__ $$aGenerative Adversarial Deep Learning with Solar Images
000905641 260__ $$c2021
000905641 300__ $$a1
000905641 3367_ $$2ORCID$$aCONFERENCE_PAPER
000905641 3367_ $$033$$2EndNote$$aConference Paper
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000905641 3367_ $$2DRIVER$$aconferenceObject
000905641 3367_ $$2DataCite$$aOutput Types/Conference Paper
000905641 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1642750586_26828
000905641 520__ $$aThe Solar Dynamics Observatory (SDO) offers an unprecedented, very large dataset of solar images in different optical and EUV wavelength bands, capturing solar atmospheric structures in high resolution and with excellent coverage since 2010. This dataset is thus well suited to study the application of advanced machine learning techniques that require large amounts of data for training, such as deep learning approaches. Here, we present results of generative adversarial deep learning as applied to a large database of solar images and discuss challenges in training and validation, in particular with distributed training on large computer clusters. We address the potential of data augmentation techniques for improved learning and image quality and the opportunities for latent space structure exploration and control. Potential application downstream that can make use of such generated images are briefly discussed and the need for a community-driven, physics-based basis to establish evaluation criteria for generative models will be emphasized.
000905641 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000905641 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
000905641 7001_ $$0P:(DE-HGF)0$$aVasile, Ruggero$$b1
000905641 7001_ $$0P:(DE-Juel1)180894$$aCherti, Mehdi$$b2$$ufzj
000905641 7001_ $$0P:(DE-Juel1)185654$$aKesselheim, Stefan$$b3$$ufzj
000905641 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b4$$ufzj
000905641 909CO $$ooai:juser.fz-juelich.de:905641$$pVDB
000905641 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Ruhr Universität Bochum$$b0
000905641 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aGFZ Potsdam$$b1
000905641 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180894$$aForschungszentrum Jülich$$b2$$kFZJ
000905641 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185654$$aForschungszentrum Jülich$$b3$$kFZJ
000905641 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)158080$$aForschungszentrum Jülich$$b4$$kFZJ
000905641 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$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
000905641 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$$x1
000905641 9141_ $$y2021
000905641 920__ $$lyes
000905641 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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000905641 980__ $$aI:(DE-Juel1)JSC-20090406
000905641 980__ $$aUNRESTRICTED