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Andreas Gerhardus
Andreas Gerhardus
Institute of Data Science, German Aerospace Center (DLR)
Bestätigte E-Mail-Adresse bei dlr.de
Titel
Zitiert von
Zitiert von
Jahr
Search for the effect of massive bodies on atomic spectra and constraints on Yukawa-type interactions of scalar particles
N Leefer, A Gerhardus, D Budker, VV Flambaum, YV Stadnik
Physical review letters 117 (27), 271601, 2016
472016
High-recall causal discovery for autocorrelated time series with latent confounders
A Gerhardus, J Runge
Advances in Neural Information Processing Systems 33, 12615-12625, 2020
432020
Quantum periods of Calabi–Yau fourfolds
A Gerhardus, H Jockers
Nuclear Physics B 913, 425-474, 2016
352016
Dual pairs of gauged linear sigma models and derived equivalences of Calabi–Yau threefolds
A Gerhardus, H Jockers
Journal of Geometry and Physics 114, 223-259, 2017
232017
The geometry of gauged linear sigma model correlation functions
A Gerhardus, H Jockers, U Ninad
Nuclear Physics B 933, 65-133, 2018
152018
Supersymmetric black holes and the SJT/nSCFT1 correspondence
S Förste, A Gerhardus, J Kames-King
Journal of High Energy Physics 2021 (1), 1-44, 2021
72021
Discovering causal relations and equations from data
G Camps-Valls, A Gerhardus, U Ninad, G Varando, G Martius, ...
arXiv preprint arXiv:2305.13341, 2023
12023
A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections
XA Tibau, C Reimers, A Gerhardus, J Denzler, V Eyring, J Runge
Environmental Data Science 1, e12, 2022
12022
Characterization of causal ancestral graphs for time series with latent confounders
A Gerhardus
arXiv preprint arXiv:2112.08417, 2021
12021
String Compactifications from the Worldsheet and Target Space Point of View
A Gerhardus
Universitäts-und Landesbibliothek Bonn, 2019
12019
Formalising causal inference in time and frequency on process graphs with latent components
ND Reiter, A Gerhardus, J Wahl, J Runge
arXiv preprint arXiv:2305.11561, 2023
2023
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
T Beucler, FIH Tam, MS Gomez, J Runge, A Gerhardus
arXiv preprint arXiv:2304.05294, 2023
2023
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery
S Saranya Ganesh, T Beucler, F Iat-Hin Tam, MS Gomez, J Runge, ...
arXiv e-prints, arXiv: 2304.05294, 2023
2023
Causal Discovery to Improve Machine Learning-Based Tropical Cyclone Intensity Predictions
SG Sudheesh, TG Beucler, FIH Tam, A Gerhardus, J Runge
103rd AMS Annual Meeting, 2023
2023
A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections–CORRIGENDUM
XA Tibau, C Reimers, A Gerhardus, J Denzler, V Eyring, J Runge
Environmental Data Science 2, e4, 2023
2023
Understanding and predicting the interannual variability (IAV) of the global terrestrial carbon cycle
J Wen, J Runge, A Gerhardus, Y SUN
AGU Fall Meeting Abstracts 2022, B42H-1725, 2022
2022
Causal inference for temporal patterns
ND Reiter, A Gerhardus, J Runge
arXiv preprint arXiv:2205.15149, 2022
2022
Causal Discovery in Ensembles of Climate Time Series
A Gerhardus, J Runge
EGU General Assembly Conference Abstracts, EGU22-6958, 2022
2022
Causal Orthogonal Functions: A Causal Inference approach to temporal feature extraction
ND Reiter, J Runge, A Gerhardus
EGU General Assembly Conference Abstracts, EGU22-9112, 2022
2022
Reliable causal discovery in time series
A Gerhardus
2022
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