E-LIB Suche - Ergebnisse für: Draxl, C.

1

Forecasting for the weather driven energy system - a new ta..:

, In: 21st Wind & Solar Integration Workshop (WIW 2022),

Giebel, G. ; Draxl, C. ; Frank, H.... - p. None , 2022

Link: https://doi.org/10.1049/..

2

First-principles study of elastic and thermal properties of..:

Najafvandzadeh, N. ; López-Moreno, S. ; Errandonea, D...
Materials Today Communications. 24 (2020) - p. 101089 , 2020

Link: https://doi.org/10.1016/..

3

Spatial Variability of Winds and HRRR–NCEP Model Error Stat..:

Pichugina, Y. L. ; Banta, R. M. ; Bonin, T....
Journal of Applied Meteorology and Climatology. 58 (2019) 8 - p. 1633-1656 , 2019

Link: https://www.jstor.org/st..

4

Coupling of Hubbard fermions with phonons in La2 CuO4: A co..:

Shneyder, E.I. ; Spitaler, J. ; Kokorina, E.E....
Journal of Alloys and Compounds. 648 (2015) - p. 258-264 , 2015

Link: https://doi.org/10.1016/..

5

The Be K-edge in beryllium oxide and chalcogenides: soft x-..:

Olovsson, W ; Weinhardt, L ; Fuchs, O...
Journal of Physics: Condensed Matter. 25 (2013) 31 - p. 315501 , 2013

Link: https://doi.org/10.1088/..

6

Diffusive and massive phase transformations in Ti–Al–Nb all..:

Gamsjäger, E. ; Liu, Y. ; Rester, M....
Intermetallics. 38 (2013) - p. 126-138 , 2013

Link: https://doi.org/10.1016/..

7

IEA Wind Task 51 "Forecasting for the Weather Driven Energy..:

Giebel, Gregor ; Frank, H ; Draxl, C...
Giebel , G , Frank , H , Draxl , C , Zack , J , Browell , J , Möhrlen , C , Kariniotakis , G , Bessa , R & Lenaghan , D , IEA Wind Task 51 "Forecasting for the Weather Driven Energy System" , 2023 , Sound/Visual production (digital) .. , 2023

Link: https://orbit.dtu.dk/en/..

8

Roadmap on electronic structure codes in the exascale era:

Gavini, V ; Baroni, S ; Blum, V...
https://discovery.ucl.ac.uk/id/eprint/10176040/1/Gavini_2023_Modelling_Simul._Mater._Sci._Eng._31_063301.pdf. , 2023

Link: https://discovery.ucl.ac..

9

Roadmap on Machine learning in electronic structure:

Kulik, H J ; Hammerschmidt, T ; Schmidt, J...
Electronic Structure -- EST -- Electron. Struct. -- 2516-1075. , 2022

Link: https://doi.org/10.1088/..

10

Roadmap on machine learning in electronic structure:

Kulik, H. J ; Hammerschmidt, T ; Schmidt, J...
Kulik , H J , Hammerschmidt , T , Schmidt , J , Botti , S , Marques , M A L , Boley , M , Scheffler , M , Todorović , M , Rinke , P , Oses , C , Smolyanyuk , A , Curtarolo , S , Tkatchenko , A , Bartók , A P , Manzhos , S , Ihara , M , Carrington , T , Behler , J , Isayev , O , Veit , M , Grisafi , A , Nigam , J , Ceriotti , M , Schütt , K T , Westermayr , J , Gastegger , M , Maurer , R J , Kalita , B , Burke , K , Nagai , R , Akashi , R , Sugino , O , Hermann , J , Noé , F , Pilati , S , Draxl , C , Kuban , M , Rigamonti , S , Scheidgen , M , Esters , M , Hicks , D , Toher , C , Balachandran , P V , Tamblyn , I , Whitelam , S , Bellinger , C & Ghiringhelli , L M 2022 , ' Roadmap on machine learning in electronic structure ' , Electronic Structure , vol. 4 , no. 2 , 023004 . https://doi.org/10.1088/2516-1075/ac572f. , 2022

Link: https://research.monash...

11

Roadmap on Machine learning in electronic structure:

Kulik, H J ; Hammerschmidt, T ; Schmidt, J...
http://edoc.hu-berlin.de/18452/28260. , 2022

Link: http://edoc.hu-berlin.de..

12

Roadmap on Machine learning in electronic structure:

Kulik, H. J ; Hammerschmidt, T ; Schmidt, J...
https://refubium.fu-berlin.de/handle/fub188/36265. , 2022

Link: https://refubium.fu-berl..

13

Roadmap on Machine learning in electronic structure:

Kulik, H. J ; Hammerschmidt, T ; Schmidt, J...
2516-1075. , 2022

Link: https://resolver.sub.uni..

14

FAIR data enabling new horizons for materials research:

Scheffler, M ; Aeschlimann, M ; Albrecht, M...
info:eu-repo/semantics/altIdentifier/wos/000788315300009. , 2022

Link: https://publikationen.bi..

15

Roadmap on Machine learning in electronic structure:

Kulik, H. J ; Hammerschmidt, T ; Schmidt, J...
info:eu-repo/grantAgreement/EC/H2020/951786/EU//NOMAD CoE Funding Information: This work received funding from the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No. 676580), and from the Deutsche Forschungsgemeinschaft (DFG), projects 414984028 (SFB 1404, FONDA) and 460197019 (NFDI consortium FAIRmat). Funding Information: This work was funded by the Austrian Science Fund (FWF) [J 4522-N] (JW), the Federal Ministry of Education and Research (BMBF) for the Berlin Center for Machine Learning/BIFOLD (01IS18037A) (KTS), and the UKRI Future Leaders Fellowship programme (MR/S016023/1) (RJM). MG works at the BASLEARN-TU Berlin/BASF Joint Lab for Machine Learning, co-financed by TU Berlin and BASF SE. Funding Information: Financial support by the Deutsche Forschungsgemeinschaft (DFG) through Project C1 of the collaborative research centre SFB/TR 103 'From Atoms to Turbine Blades—A Scientific Basis for a new Generation of Single-Crystal Supe.... , 2022

Link: https://aaltodoc.aalto.f..