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RISS 인기검색어
- 검색키워드
- 저자 : Schu¨ller, Ulrich (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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SchNetPack: A Deep Learning Toolbox For Atomistic Systems
Schü,tt, K. T.,Kessel, P.,Gastegger, M.,Nicoli, K. A.,Tkatchenko, A.,Mü,ller, K.-R. American Chemical Society 2019 Journal of chemical theory and computation Vol.15 No.1
<P>SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.</P> [FIG OMISSION]</BR>
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On procedures for reliability assessment of mechanical systems and structures
G. I. Schuëller 국제구조공학회 2007 Structural Engineering and Mechanics, An Int'l Jou Vol.25 No.3
In this paper a brief overview of methods to assess the reliability of mechanical systems and structures is presented. A selection of computational procedures, stochastic structural dynamics, stochastic fatigue crack growth and reliability based optimization are discussed. It is shown that reliability based methods may form the basis for a rational decision making.
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The effects of uncertainties in structural analysis
M. F. Pellissetti,G. I. Schuëller 국제구조공학회 2007 Structural Engineering and Mechanics, An Int'l Jou Vol.25 No.3
Model-based predictions of structural behavior are negatively affected by uncertainties of various type and in various stages of the structural analysis. The present paper focusses on dynamic analysis and addresses the effects of uncertainties concerning material and geometric parameters, mainly in the context of modal analysis of large-scale structures. Given the large number of uncertain parameters arising in this case, highly scalable simulation-based methods are adopted, which can deal with possibly thousands of uncertain parameters. In order to solve the reliability problem, i.e., the estimation of very small exceedance probabilities, an advanced simulation method called Line Sampling is used. In combination with an efficient algorithm for the estimation of the most important uncertain parameters, the method provides good estimates of the failure probability and enables one to quantify the error in the estimate. Another aspect here considered is the uncertainty quantification for closely-spaced eigenfrequencies. The solution here adopted represents each eigenfrequency as a weighted superposition of the full set of eigenfrequencies. In a case study performed with the FE model of a satellite it is shown that the effects of uncertain parameters can be very different in magnitude, depending on the considered response quantity. In particular, the uncertainty in the quantities of interest (eigenfrequencies) turns out to be mainly caused by very few of the uncertain parameters, which results in sharp estimates of the failure probabilities at low computational cost.
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Jurmeister, Philipp,Bockmayr, Michael,Seegerer, Philipp,Bockmayr, Teresa,Treue, Denise,Montavon, Gré,goire,Vollbrecht, Claudia,Arnold, Alexander,Teichmann, Daniel,Bressem, Keno,Schu¨ller, Ulrich American Association for the Advancement of Scienc 2019 Science translational medicine Vol.11 No.509
<P>Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.</P>
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