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arXiv 2023年

作者： Maier, Gregor Hamaekers, Jan Martilotti, Dominik-Sergio Ziebarth, Benedikt Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53757 Germany Institut für Numerische Simulation Universität Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53757 Germany Schott AG Hattenbergstrasse 10 Mainz55122 Germany

Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest. Current machine learning methods for learning the composition-property relationship of glasses promise to save on expensive trial-and-error approaches. Even though quite large datasets on the composition of glasses and their properties already exist (i.e., with more than 350,000 samples), they cover only a very small fraction of the space of all possible glass compositions. This limits the applicability of purely data-driven models for property prediction purposes and necessitates the development of models with high extrapolation power. In this paper, we propose a neural network model which incorporates prior scientific and expert knowledge in its learning pipeline. This informed learning approach leads to an improved extrapolation power compared to blind (uninformed) neural network models. To demonstrate this, we train our models to predict three different material properties, that is, the glass transition temperature, the Young’s modulus (at room temperature), and the shear modulus of binary oxide glasses which do not contain sodium. As representatives for conventional blind neural network approaches we use five different feed-forward neural networks of varying widths and depths. For each property, we set up model ensembles of multiple trained models and show that, on average, our proposed informed model performs better in extrapolating the three properties of previously unseen sodium borate glass samples than all five conventional blind models. © 2023, CC BY-SA.

关键词： Extrapolation

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Brain based BMIgap as a new tool to correlate obesity and neural alterations – a multicohort study

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Neuroscience Applied 2022年 1卷

作者： A. Khuntia R. Paul O.A. Andreassen F. Degenhard R. Eils J. Erdmann C. Herrmann M. Hofmann-Apitius T. Kaufmann A.T. Kodamullil S. Mucha M.M. Nöthen M.L. Pedersen A. Quintero H. Schunkert A. Sharma H. Tost L.T. Westlye Y. Zhang J. Kambeitz N. Koutsouleris 1Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University Psychiatry Munich Germany 2Max Plank Institute of Psychiatry Psychiatry Munich Germany 3Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University- Munich- Germany Department of Psychiatry and Psychotherapy München Germany 4Norwegian Centre for Mental Disorders Research NORMENT Division of Mental Health and Addiction Oslo Norway 5Institute of Human Genetics- University of Bonn School of Medicine Bonn Germany 6Health Data Science Unit Heidelberg University Hospital- and BioQuant Heidelberg Germany 7Institute for Cardiogenetics University of Lübeck Lübeck Germany 8Heidelberg University Hospital- and BioQuant Health Data Science Unit Heidelberg Germany 9Fraunhofer Institute for Algorithms and Scientific Computing SCAI Bioinformatics Sankt Augustin Germany 10Fraunhofer Institute for Algorithms and Scientific Computing SCAI- Bioinformatics Sankt Augustin Germany 11Institute for Cardiogenetics- University of Lübeck- DZHK Cardiogenetics Lübeck Germany 12Institute of Human Genetics- University of Bonn Human Genetics Bonn Germany 13University of Oslo Department of Psychology Oslo Norway 14Deutsches Herzzentrum München- Technische Universität München Department of Cardiology Munich Germany 15Heidelberg University Hospital- and BioQuant Health Data Science Unit- Heidelberg Germany 16Central Institute of Mental Health Department of Psychiatry and Psychotherapy- Mannheim Germany 17University of Cologne Department of Psychiatry and Psychotherapy- Cologne Germany 18University of Turku Department of Psychiatry- Turku Finland 19Neuroscience and Sense Organs- University of Bari Aldo Moro Department of Basic Medical Science Bari Italy 20Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Department of Neurosciences and Mental Health Milano Italy 21Institute of Mental Health- University of Birmingham Psychiatry Birmingham United Kingdom 22University of Birmingham School of Psyc

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