Zobrazeno 1 - 10
of 778
pro vyhledávání: '"Elsheikh, Ahmed A."'
Object detection is crucial in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on data from conventional frame-based RGB sensors. However, these sensors often struggle with issues like m
Externí odkaz:
http://arxiv.org/abs/2408.05321
Texture models based on Generative Adversarial Networks (GANs) use zero-padding to implicitly encode positional information of the image features. However, when extending the spatial input to generate images at large sizes, zero-padding can often lea
Externí odkaz:
http://arxiv.org/abs/2309.02340
Autor:
Shams, Mosayeb, Elsheikh, Ahmed H.
Active flow control (AFC) involves manipulating fluid flow over time to achieve a desired performance or efficiency. AFC, as a sequential optimisation task, can benefit from utilising Reinforcement Learning (RL) for dynamic optimisation. In this work
Externí odkaz:
http://arxiv.org/abs/2305.02033
Autor:
Dixit, Atish, Elsheikh, Ahmed
Reinforcement learning (RL) is a promising method to solve control problems. However, model-free RL algorithms are sample inefficient and require thousands if not millions of samples to learn optimal control policies. A major source of computational
Externí odkaz:
http://arxiv.org/abs/2210.08400
Publikováno v:
Journal of Computational Science Volume 65, November 2022, 101876
The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of geomodels, combined with Ense
Externí odkaz:
http://arxiv.org/abs/2207.03596
Autor:
Dixit, Atish, ElSheikh, Ahmed H.
Publikováno v:
Engineering Applications of Artificial Intelligence, Volume 114, 2022, 105106, ISSN 0952-1976
We present a case study of model-free reinforcement learning (RL) framework to solve stochastic optimal control for a predefined parameter uncertainty distribution and partially observable system. We focus on robust optimal well control problem which
Externí odkaz:
http://arxiv.org/abs/2207.03456
Autor:
Dixit, Atish, ElSheikh, Ahmed H.
Reinforcement learning (RL) is a promising tool to solve robust optimal well control problems where the model parameters are highly uncertain, and the system is partially observable in practice. However, RL of robust control policies often relies on
Externí odkaz:
http://arxiv.org/abs/2207.03253
Publikováno v:
First Break, Volume 39, Issue 7, Jul 2021, p. 45 - 50
Quantitative workflows utilizing real-time data to constrain ahead-of-bit uncertainty have the potential to improve geosteering significantly. Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertaint
Externí odkaz:
http://arxiv.org/abs/2207.01374
Publikováno v:
Geophysical Journal International, Volume 230, Issue 3, September 2022, Pages 1800-1817
The advent of fast sensing technologies allows for real-time model updates in many applications where the model parameters are uncertain. Bayesian algorithms, such as ensemble smoothers, offer a real-time probabilistic inversion accounting for uncert
Externí odkaz:
http://arxiv.org/abs/2205.12684
In the context of generating geological facies conditioned on observed data, samples corresponding to all possible conditions are not generally available in the training set and hence the generation of these realizations depends primary on the genera
Externí odkaz:
http://arxiv.org/abs/2205.05469