11,144 research outputs found
Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation
To achieve virtual certification for industrial design, quantifying the
uncertainties in simulation-driven processes is crucial. We discuss a
physics-constrained approach to account for epistemic uncertainty of turbulence
models. In order to eliminate user input, we incorporate a data-driven machine
learning strategy. In addition to it, our study focuses on developing an a
priori estimation of prediction confidence when accurate data is scarce.Comment: Workshop on Synergy of Scientific and Machine Learning Modeling, SynS
& ML ICM
Debiased Bayesian inference for average treatment effects
Bayesian approaches have become increasingly popular in causal inference
problems due to their conceptual simplicity, excellent performance and in-built
uncertainty quantification ('posterior credible sets'). We investigate Bayesian
inference for average treatment effects from observational data, which is a
challenging problem due to the missing counterfactuals and selection bias.
Working in the standard potential outcomes framework, we propose a data-driven
modification to an arbitrary (nonparametric) prior based on the propensity
score that corrects for the first-order posterior bias, thereby improving
performance. We illustrate our method for Gaussian process (GP) priors using
(semi-)synthetic data. Our experiments demonstrate significant improvement in
both estimation accuracy and uncertainty quantification compared to the
unmodified GP, rendering our approach highly competitive with the
state-of-the-art.Comment: NeurIPS 201
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification
In order to achieve a virtual certification process and robust designs for
turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to
be known. The formulation of turbulence closure models implies a major source
of the overall uncertainty of Reynolds-averaged Navier-Stokes simulations. We
discuss the common practice of applying a physics constrained eigenspace
perturbation of the Reynolds stress tensor in order to account for the model
form uncertainty of turbulence models. Since the basic methodology often leads
to overly generous uncertainty estimates, we extend a recent approach of adding
a machine learning strategy. The application of a data-driven method is
motivated by striving for the detection of flow regions, which are prone to
suffer from a lack of turbulence model prediction accuracy. In this way any
user input related to choosing the degree of uncertainty is supposed to become
obsolete. This work especially investigates an approach, which tries to
determine an a priori estimation of prediction confidence, when there is no
accurate data available to judge the prediction. The flow around the NACA 4412
airfoil at near-stall conditions demonstrates the successful application of the
data-driven eigenspace perturbation framework. Furthermore, we especially
highlight the objectives and limitations of the underlying methodology
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