1,721,360 research outputs found
Uncertainty Quantification of Cable Inductances and Capacitances via Mixed-Fidelity Models
Full text linkIn this paper, we investigate a mixed-fidelity approach for the uncertainty quantification of the per-unit-length (p.u.l.) capacitance and inductance of cables with random geometrical and material parameters. Polynomial chaos expansion is used to model uncertainty, whereas a numerical discretization technique is used to calculate p.u.l. inductances and capacitances. However, instead of using a model with high fidelity in both features, the results are obtained as a combination of two complementary models with mixed fidelity in each feature. Numerical examples concerning the statistical assessment of the p.u.l. inductance and capacitance matrices of two shielded cables show that similar accuracy is attained at a fraction of the computational cost compared to conventional approaches
Teacher Training and E-Learning: which model is possible?
No full textPresentazione a livello europeo e internazionale della sperimentazione del nuovo modello e-learning per la formazione in servizio degli insegnant
A Data Compression Strategy for the Efficient Uncertainty Quantification of Time-Domain Circuit Responses
Full text linkThis paper presents an innovative modeling strategy for the construction of efficient and compact surrogate models for the uncertainty quantification of time-domain responses of digital links. The proposed approach relies on a two-step methodology. First, the initial dataset of available training responses is compressed via principal component analysis (PCA). Then, the compressed dataset is used to train compact surrogate models for the reduced PCA variables using advanced techniques for uncertainty quantification and parametric macromodeling. Specifically, in this work sparse polynomial chaos expansion and least-square support-vector machine regression are used, although the proposed methodology is general and applicable to any surrogate modeling strategy. The preliminary compression allows limiting the number and complexity of the surrogate models, thus leading to a substantial improvement in the efficiency. The feasibility and performance of the proposed approach are investigated by means of two digital link designs with 54 and 115 uncertain parameters, respectively
A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression
Full text linkThis paper introduces a probabilistic machine learning framework for the uncertainty quantification (UQ) of electronic circuits based on Gaussian process regression (GPR). As opposed to classical surrogate modeling techniques, GPR inherently provides information on the model uncertainty. The main contribution of this work is twofold. First, it describes how, in an UQ scenario, the model uncertainty can be combined with the uncertainty of the input design parameters to provide confidence bounds for the statistical estimates of the system outputs, such as moments and probability distributions. These confidence bounds allows assessing the accuracy of the predicted statistics. Second, in order to deal with dynamic multi-output systems, principal component analysis (PCA) is effectively employed to compress the time-dependent output variables into a smaller set of components, for which the training of individual GPR models becomes feasible. The uncertainty on the principal components is then propagated back to the original output variables. Several application examples, ranging from a trivial RLC circuit to real-life designs, are used to illustrate and validate the advocated approach
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