1,664 research outputs found

    Configurable Software Performance Completions through Higher-Order Model Transformations

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    Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

    Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View

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    We present Im2Pano3D, a convolutional neural network that generates a dense prediction of 3D structure and a probability distribution of semantic labels for a full 360 panoramic view of an indoor scene when given only a partial observation (<= 50%) in the form of an RGB-D image. To make this possible, Im2Pano3D leverages strong contextual priors learned from large-scale synthetic and real-world indoor scenes. To ease the prediction of 3D structure, we propose to parameterize 3D surfaces with their plane equations and train the model to predict these parameters directly. To provide meaningful training supervision, we use multiple loss functions that consider both pixel level accuracy and global context consistency. Experiments demon- strate that Im2Pano3D is able to predict the semantics and 3D structure of the unobserved scene with more than 56% pixel accuracy and less than 0.52m average distance error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S

    Comparative Analysis of Artificial Intelligence and Numerical Reservoir Simulation in Marcellus Shale Wells

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    This dissertation addresses the limitations of conventional numerical reservoir simulation techniques in the context of unconventional shale plays and proposes the use of data-driven artificial intelligence (AI) models as a promising alternative. Traditional methods, while providing valuable insights, often rely on simplifying assumptions and are constrained by time, resources, and data quality. The research leverages AI models to handle the complexities of shale behavior more effectively, facilitating accurate predictions and optimizations with less resource expenditure. Two specific methodologies are investigated for this purpose: traditional numerical reservoir simulations using Computer Modelling Group\u27s GEM reservoir simulation software, and an AI-based Shale Analytics approach using IMPROVEâ„¢ software from Intelligent Solutions, Inc. The investigation covers the impact of key parameters on production prediction, assumptions made, predictive accuracy, data requirements, workflow complexity, and time efficiency. By comparing these methods, the research aims to offer guidelines for incorporating AI models into reservoir simulation and identify areas for increased efficiency and accuracy. The study concludes by presenting recommendations to advance the field of reservoir simulation and encourage the adoption of innovative methodologies in the energy industry. The results are anticipated to considerably enhance reservoir simulation processes and optimize production strategies for unconventional shale plays

    Deformable Shape Completion with Graph Convolutional Autoencoders

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    The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single or multiple partial scans has received increasing attention in recent years. In this work, we propose a novel learning-based method for the completion of partial shapes. Unlike the majority of existing approaches, our method focuses on objects that can undergo non-rigid deformations. The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the representation in this latent space that best fits the generated shape to the known partial input. The completed shape exhibits a realistic appearance on the unknown part. We show promising results towards the completion of synthetic and real scans of human body and face meshes exhibiting different styles of articulation and partiality.Comment: CVPR 201

    Liquids-Rich Shale Evaluation: Modelling and Optimization of Hydraulically Fractured Liquids-Rich Shale Wells

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