A machine learning approach for efficient uncertainty quantification using multiscale methods

Several multiscale methods account for sub-grid scale features using coarse scale basis functions. For example, in the Multiscale Finite Volume method the coarse scale basis functions are obtained by solving a set of local problems over dual-grid cells. We introduce a data-driven approach for the estimation of these coarse scale basis functions. Specifically, we employ a neural network predictor fitted using a set of solution samples from which it learns to generate subsequent basis functions at a lower computational cost than solving the local problems. The computational advantage of this approach is realized for uncertainty quantification tasks where a large number of realizations has to be evaluated. We attribute the ability to learn these basis functions to the modularity of the local problems and the redundancy of the permeability patches between samples. The proposed method is evaluated on elliptic problems yielding very promising results.Comment: Journal of Computational Physics (2017

Parametrization of stochastic inputs using generative adversarial networks with application in geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude

Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models

Surrogate-modelling techniques including Polynomial Chaos Expansion (PCE) is commonly used for statistical estimation (aka. Uncertainty Quantification) of quantities of interests obtained from expensive computational models. PCE is a data-driven regression-based technique that relies on spectral polynomials as basis-functions. In this technique, the outputs of few numerical simulations are used to estimate the PCE coefficients within a regression framework combined with regularization techniques where the regularization parameters are estimated using standard cross-validation as applied in supervised machine learning methods. In the present work, we introduce an efficient method for estimating the PCE coefficients combining Elastic Net regularization with a data-driven feature ranking approach. Our goal is to increase the probability of identifying the most significant PCE components by assigning each of the PCE coefficients a numerical value reflecting the magnitude of the coefficient and its stability with respect to perturbations in the input data. In our evaluations, the proposed approach has shown high convergence rate for high-dimensional problems, where standard feature ranking might be challenging due to the curse of dimensionality. The presented method is implemented within a standard machine learning library (Scikit-learn) allowing for easy experimentation with various solvers and regularization techniques (e.g. Tikhonov, LASSO, LARS, Elastic Net) and enabling automatic cross-validation techniques using a widely used and well tested implementation. We present a set of numerical tests on standard analytical functions, a two-phase subsurface flow model and a simulation dataset for CO2 sequestration in a saline aquifer. For all test cases, the proposed approach resulted in a significant increase in PCE convergence rates.Comment: 28 pages, 7 figures, published in Journal of Computational Physics (2019

Generating Infinite-Resolution Texture using GANs with Patch-by-Patch Paradigm

In this paper, we introduce a novel approach for generating texture images of infinite resolutions using Generative Adversarial Networks (GANs) based on a patch-by-patch paradigm. Existing texture synthesis techniques often rely on generating a large-scale texture using a one-forward pass to the generating model, this limits the scalability and flexibility of the generated images. In contrast, the proposed approach trains GANs models on a single texture image to generate relatively small patches that are locally correlated and can be seamlessly concatenated to form a larger image while using a constant GPU memory footprint. Our method learns the local texture structure and is able to generate arbitrary-size textures, while also maintaining coherence and diversity. The proposed method relies on local padding in the generator to ensure consistency between patches and utilizes spatial stochastic modulation to allow for local variations and diversity within the large-scale image. Experimental results demonstrate superior scalability compared to existing approaches while maintaining visual coherence of generated textures

Post Fire Behavior of Structural Reinforced Concrete Member (Slab) Repairing with Various Materials

One of the most significant building materials used to build a variety of infrastructure, military, and civil structures is concrete. It can effectively withstand fire mishaps for a long period of time. This study employs a finite element simulation approach in Three steps: the first involves applying mechanical loading, the second involves applying mechanical and thermal loading; and the third involves strengthening the damaged model. Two different strengthening procedures were used to evaluate the performance of the fire-damaged slab. Two types of strengthening techniques—carbon-fiber-reinforced polymer (CFRP) sheet and slurry-infiltrated fibrous concrete (SIFCON) jacketing—were used. Studying the performance of SIFCON and CFRP together and in two different thicknesses of each for repairing both normal and high-strength concretes after fire exposure is considered limited. An investigation of their behavior can provide insights into how effective the restoration of strength is. The study aims to assess how well various repair materials perform in restoring the durability and strength of reinforced concrete members after being exposed to fire. This will assist in determining the best materials for concrete repair after a fire. Results show that the enhancements by SIFCON with a thickness of 30 mm significantly improved many indices, including load displacement behavior, ductility, and absorption energy of the slab. Doi: 10.28991/CEJ-2023-09-08-013 Full Text: PD

The effect of experimental fascioliasis on the pharmacokinetics of antipyrine and sulphadimidine in desert sheep

Healthy adult male desert sheep were experimentally infected with Fasciola gigantica, to investigate the influence of experimental fasciolasis on the pharmacokinetics of antipyrine and sulphadimidine. Each animal received 500 metacercariae orally. The experimental infection was confirmed histologically, by detection of Fasciola eggs in faeces and by measuring the activities of the enzymes sorbitol dehydrogenase (SD), glutamate dehydrogenase (GD) and aspartate aminotransferase (AST) in plasma during the course of the disease. Changes in the pharmacokinetics of antipyrine and sulphadimidine were reported in the experimentally infected animals. Significant prolongation of antipyrine half life was observed 16 weeks after infection. The half-life of sulphadimidine was also significantly prolonged 5, 9 and 16 weeks after infection. Clearance of the sulphonamide was decreased significantly 5 and 9 weeks after infection and it regained its pre-infection value 16 weeks after infection