Nonparametric Uncertainty Quantification for Stochastic Gradient Flows

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Nonparametric Budgeted Stochastic Gradient Descent

One of the most challenging problems in kernel online learning is to bound the model size. Budgeted kernel online learning addresses this issue by bounding the model size to a predefined budget. However, determining an appropriate value for such predefined budget is arduous. In this paper, we propose the Nonparametric Budgeted Stochastic Gradient Descent that allows the model size to automatica...

متن کامل

Mixed Aleatory/Epistemic Uncertainty Quantification for Hypersonic Flows via Gradient-Based Optimization and Surrogate Models

The use of optimization for the propagation of mixed epistemic/aleatory uncertainties is demonstrated within the context of hypersonic flows. Specifically, this work focuses on strategies applicable for models where input parameters can be divided into a set of variables containing only aleatory uncertainties and a set with epistemic uncertainties. With the input parameters divided in this way,...

متن کامل

Sparse multiresolution stochastic approximation for uncertainty quantification

Most physical systems are inevitably affected by uncertainties due to natural variabili-ties or incomplete knowledge about their governing laws. To achieve predictive computer simulations of such systems, a major task is, therefore, to study the impact of these uncertainties on response quantities of interest. Within the probabilistic framework, uncertainties may be represented in the form of r...

متن کامل

Supplementary Material for Nonparametric Budgeted Stochastic Gradient Descent

1 Notion We introduce some notions used in this supplementary material. For regression task, we define y max = max y |y|. We further denote the set S as S = B 0, y max λ −1/2 if L2 is used and λ ≤1 R D otherwise where B 0, y max λ −1/2 = w ∈ R D : w ≤ y max λ −1/2 and R D specifies the whole feature space. We introduce five types of loss functions that can be used in our proposed algorithm, nam...

متن کامل

Uncertainty Quantification for Turbulent Mixing Flows: Rayleigh-Taylor Instability

Uncertainty Quantification (UQ) for fluid mixing depends on the length scales for observation: macro, meso and micro, each with its own UQ requirements. New results are presented here for macro and micro observables. For the micro observables, recent theories argue that convergence of numerical simulations in Large Eddy Simulations (LES) should be governed by space-time dependent probability di...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: SIAM/ASA Journal on Uncertainty Quantification

سال: 2015

ISSN: 2166-2525

DOI: 10.1137/14097940x