Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Schmerler, S"'
Autor:
Schmerler, S., Steinbach, P.
Publikováno v:
Helmholtz AI conference, 02.-03.06.2022, Dresden, Germany
Test whether two sets of points are samples from the same D-dimensional probability distribution without having access to the PDF.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::172b904596fe68338365ed3f18ceca3d
https://www.hzdr.de/publications/Publ-35437-1
https://www.hzdr.de/publications/Publ-35437-1
Autor:
Fiedler, L., Schmerler, S., Modine, N., Vogel, D. J., Popoola, G. A., Thompson, A., Rajamanickam, S., Cangi, A.
Publikováno v:
Publication date: 2022-09-30 Open accessDOI: 10.14278/rodare.1850Versions: 10.14278/rodare.1851License: CC-BY-4.0
Scripts and Models for "Predicting the Electronic Structure of Matter on Ultra-Large Scales" This data set contains scripts and models to reproduce the results of our manuscript "Physics-informed Machine Learning Models for Scalable Density Functiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::42411e003d78efc3bd2e89247e9e4260
https://www.hzdr.de/publications/Publ-35305-1
https://www.hzdr.de/publications/Publ-35305-1
Autor:
Schmerler, S., Starke, S., Steinbach, P., M. K. Siddiqui, Q., Fiedler, L., Cangi, A., Kulkarni, S. H.
Publikováno v:
Helmholtz AI Evaluation 2022, 05.-07.10.2022, München, Germany
We strive to popularize the usage of uncertainty quantification methods in machine learning through publications and application in various projects covering diverse fields from regression and classification to instance segmentation. In addition, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::56ff3a7f89c1878ac884bcfe9a938a48
https://www.hzdr.de/publications/Publ-35454-1
https://www.hzdr.de/publications/Publ-35454-1
Publikováno v:
Big data analytical methods for complex systems, 06.-07.10.2022, Wroclaw, Poland
Understanding the properties of matter under extreme conditions is essential for advancing our fundamental understanding of astrophysical objects and guides the search for exoplanets, it propels the discovery of materials exhibiting novel properties
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::a20b25a71719e3f34c54c962250a71cc
https://www.hzdr.de/publications/Publ-35451-1
https://www.hzdr.de/publications/Publ-35451-1
Autor:
Schmerler, S.
An introduction to Automatic Differentiation with theory and code examples.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::68347293fca4b6b5fc780dbbc3f9898c
https://www.hzdr.de/publications/Publ-32764-1
https://www.hzdr.de/publications/Publ-32764-1
Publikováno v:
Publication year 2021Programming language: pythonSystem requirements: Linux, Windows or macOSLicense: BSD-3 clause (Link to license text) Hosted on zenodo: Link to location
A jupyter notebook that can predict the shoe size of a person based on their gender, height and weight. This is a notebook meant for training purposes to show case how public data can be used to train a machine learning predictor. This notebook uses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::b742f1a7d245cddb1061ec248d813d53
https://www.hzdr.de/publications/Publ-33191-1
https://www.hzdr.de/publications/Publ-33191-1
Autor:
Steinbach, P., Schmerler, S.
Publikováno v:
Publication year 2021License: Creative Commons Attribution 4.0 International (Link to license text) Hosted on zenodo: Link to location
This data set was crowdsourced at the 2021 Helmholtz MT ARD ST3 meeting from attendants of the Machine Learning Tutorial on Sep 30, 2021. For more details on the event, see https://indico.desy.de/event/28823/
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::1985ea38326cfbf4adf232d6d6c33461
https://www.hzdr.de/publications/Publ-33190-1
https://www.hzdr.de/publications/Publ-33190-1
Publikováno v:
7. Annual MT Meeting, 16.-18.06.2021, Online, Germany
The successful characterization of high energy density (HED) phenomena in experimental facilities is possible only with numerical modeling. The persistence of electron correlation in HED matter is one of the greatest challenges for accurate numerical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::5a97112d7c9eaa6d96cd2bf28113e0ea
https://www.hzdr.de/publications/Publ-33763-1
https://www.hzdr.de/publications/Publ-33763-1
Autor:
Cangi, A., Ellis, J. A., Fiedler, L., Kotik, D., Modine, N. A., Oles, V., Popoola, G. A., Rajamanickam, S., Schmerler, S., Stephens, J. A., Thompson, A. P.
Publikováno v:
Publication year 2021Programming language: PythonSystem requirements: noneLicense: BSD 3 (Link to license text) Hosted on https://github.com/mala-project/mala/: Link to location
MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive step
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______4577::f57e26963f1ef1bf2d4f14b2ab17a5a2
https://www.hzdr.de/publications/Publ-33818-1
https://www.hzdr.de/publications/Publ-33818-1
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