Zobrazeno 1 - 10
of 657
pro vyhledávání: '"M. Noack"'
Autor:
C. P. O. Reyer, R. Silveyra Gonzalez, K. Dolos, F. Hartig, Y. Hauf, M. Noack, P. Lasch-Born, T. Rötzer, H. Pretzsch, H. Meesenburg, S. Fleck, M. Wagner, A. Bolte, T. G. M. Sanders, P. Kolari, A. Mäkelä, T. Vesala, I. Mammarella, J. Pumpanen, A. Collalti, C. Trotta, G. Matteucci, E. D'Andrea, L. Foltýnová, J. Krejza, A. Ibrom, K. Pilegaard, D. Loustau, J.-M. Bonnefond, P. Berbigier, D. Picart, S. Lafont, M. Dietze, D. Cameron, M. Vieno, H. Tian, A. Palacios-Orueta, V. Cicuendez, L. Recuero, K. Wiese, M. Büchner, S. Lange, J. Volkholz, H. Kim, J. A. Horemans, F. Bohn, J. Steinkamp, A. Chikalanov, G. P. Weedon, J. Sheffield, F. Babst, I. Vega del Valle, F. Suckow, S. Martel, M. Mahnken, M. Gutsch, K. Frieler
Publikováno v:
Earth System Science Data, Vol 12, Pp 1295-1320 (2020)
Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and relia
Externí odkaz:
https://doaj.org/article/f68b0e61c8f44e069624274d85354fdf
Autor:
John C. Thomas, Wei Chen, Yihuang Xiong, Bradford A. Barker, Junze Zhou, Weiru Chen, Antonio Rossi, Nolan Kelly, Zhuohang Yu, Da Zhou, Shalini Kumari, Edward S. Barnard, Joshua A. Robinson, Mauricio Terrones, Adam Schwartzberg, D. Frank Ogletree, Eli Rotenberg, Marcus M. Noack, Sinéad Griffin, Archana Raja, David A. Strubbe, Gian-Marco Rignanese, Alexander Weber-Bargioni, Geoffroy Hautier
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here
Externí odkaz:
https://doaj.org/article/47f18c8267ef4b4aaef3c2384dba21c9
Publikováno v:
APL Machine Learning, Vol 2, Iss 1, Pp 010902-010902-27 (2024)
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning (ML) in the last two decades because of their superi
Externí odkaz:
https://doaj.org/article/079ada3d79f547e7bb3ac57fa0d22489
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP’s analytical tractability, robustness, and natural inc
Externí odkaz:
https://doaj.org/article/692e0779538c48658f72f34eada9e421
Autor:
John C. Thomas, Antonio Rossi, Darian Smalley, Luca Francaviglia, Zhuohang Yu, Tianyi Zhang, Shalini Kumari, Joshua A. Robinson, Mauricio Terrones, Masahiro Ishigami, Eli Rotenberg, Edward S. Barnard, Archana Raja, Ed Wong, D. Frank Ogletree, Marcus M. Noack, Alexander Weber-Bargioni
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-7 (2022)
Abstract Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this
Externí odkaz:
https://doaj.org/article/70c42f9b6d544010bbb71447638d0527
Publikováno v:
Adsorption Science & Technology, Vol 3 (1986)
The adsorption properties of AlPO 4 -5 samples have been studied by kinetic and equilibrium measurements using hydrocarbons and water as adsorbates. From the adsorption of hydrocarbons a non-polar surface of the AlPO 4 -5 molecular sieve is concluded
Externí odkaz:
https://doaj.org/article/0b3ae510eeed4b70a09d42a9266571a5
Autor:
Stephen J. Harris, Marcus M. Noack
Publikováno v:
Joule. 7:920-934
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98de65710d6773baca672770ae4d0ed8
https://doi.org/10.1016/j.joule.2023.03.008
https://doi.org/10.1016/j.joule.2023.03.008
Autor:
Marcus M. Noack, James A. Sethian
Publikováno v:
Communications in Applied Mathematics and Computational Science. 17:131-156
Gaussian process regression is a widely-applied method for function approximation and uncertainty quantification. The technique has gained popularity recently in the machine learning community due to its robustness and interpretability. The mathemati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f761b20738e3961e0b51e5ceb1b48234
https://doi.org/10.2140/camcos.2022.17.131
https://doi.org/10.2140/camcos.2022.17.131
Publikováno v:
River Research and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d9b7ef7ba2eebcbae2b8b8521010952
https://doi.org/10.1002/rra.4145
https://doi.org/10.1002/rra.4145
Autor:
J. A. Sethian, J. J. Donatelli, A. Hexemer, M. M. Noack, D. M. Pelt, D. M. Ushizima, P. H. Zwart
Publikováno v:
Artificial Intelligence for Science ISBN: 9789811265662
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0c2b5e873d2c940a93b84c6fa21b631
https://doi.org/10.1142/9789811265679_0008
https://doi.org/10.1142/9789811265679_0008
