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11 |
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023†
Poltavsky, Igor. - Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2025
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12 |
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023†
Poltavsky, Igor. - Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2025
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13 |
Detection of particle contamination and lubrication outage in journal bearings in wind turbine gearboxes using surface acoustic wave measurements and machine learning
Decker, Thomas Matthias. - Aachen : Universitätsbibliothek der RWTH Aachen, 2025
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14 |
Discussion on Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models
Steland, Ansgar. - Aachen : Universitätsbibliothek der RWTH Aachen, 2025
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15 |
Dynamic deep learning based super-resolution for the shallow water equations
Witte, Maximilian. - Hamburg : Technische Universität Hamburg. Universitätsbibliothek, 2025
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16 |
Editorial: Integrating machine learning with physics-based modeling of physiological systems
Lee, Jae H.. - Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2025
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17 |
Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques
Adedara, Muyiwa Lawrence. - Kiel : Universitätsbibliothek Kiel, 2025
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18 |
Flexible development and evaluation of machine‐learning‐supported optimal control and estimation methods via HILO‐MPC
Pohlodek, Johannes. - Darmstadt : Universitäts- und Landesbibliothek, 2025
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19 |
Flugmedizinische Risikostratifizierung – gestern, heute, morgen
Güttler, Norbert. - Bonn : Fachinformationszentrum der Bundeswehr, 2025
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20 |
Forecasting of residential unit's heat demands: a comparison of machine learning techniques in a real-world case study
Kemper, Neele. - Augsburg : Universität Augsburg, 2025
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