Zobrazeno 1 - 10 of 654 pro vyhledávání: '"Scholten, Thomas"'
1
Report
SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction
Autor: Kakhani, Nafiseh, Rangzan, Moien, Jamali, Ali, Attarchi, Sara, Alavipanah, Seyed Kazem, Mommert, Michael, Tziolas, Nikolaos, Scholten, Thomas
Soil Organic Carbon (SOC) constitutes a fundamental component of terrestrial ecosystem functionality, playing a pivotal role in nutrient cycling, hydrological balance, and erosion mitigation. Precise mapping of SOC distribution is imperative for the
Externí odkaz: http://arxiv.org/abs/2308.03586
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2
Report
Inductive biases in deep learning models for weather prediction
Autor: Thuemmel, Jannik, Karlbauer, Matthias, Otte, Sebastian, Zarfl, Christiane, Martius, Georg, Ludwig, Nicole, Scholten, Thomas, Friedrich, Ulrich, Wulfmeyer, Volker, Goswami, Bedartha, Butz, Martin V.
Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the la
Externí odkaz: http://arxiv.org/abs/2304.04664
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3
Akademický článek
Rule-based analysis of throughfall kinetic energy to evaluate biotic and abiotic factor thresholds to mitigate erosive power
Autor: Goebes, Philipp, Schmidt, Karsten, Stumpf, Felix, von Oheimb, Goddert, Scholten, Thomas, Härdtle, Werner, Seitz, Steffen
Below vegetation, throughfall kinetic energy (TKE) is an important factor to express the potential of rainfall to detach soil particles and thus for predicting soil erosion rates. TKE is affected by many biotic (e.g. tree height, leaf area index) and
Externí odkaz: https://tud.qucosa.de/id/qucosa%3A35382
https://tud.qucosa.de/api/qucosa%3A35382/attachment/ATT-0/
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9
Report
Latent State Inference in a Spatiotemporal Generative Model
Autor: Karlbauer, Matthias, Menge, Tobias, Otte, Sebastian, Lensch, Hendrik P. A., Scholten, Thomas, Wulfmeyer, Volker, Butz, Martin V.
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors from time series data without supervision remains an open challenge. He
Externí odkaz: http://arxiv.org/abs/2009.09823
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10
Report
Inferring, Predicting, and Denoising Causal Wave Dynamics
Autor: Karlbauer, Matthias, Otte, Sebastian, Lensch, Hendrik P. A., Scholten, Thomas, Wulfmeyer, Volker, Butz, Martin V.
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally parameterizable laterally connected network modules. DISTANA is specifically d
Externí odkaz: http://arxiv.org/abs/2009.09187
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