Publications

@inproceedings{ansari_augmented_2024,

title = {Augmented Reality-Driven Reservoir Management Via Generative Ai: Transforming Pore-Scale Fluid Flow Simulation},

author = {U. Ansari and H. A. Qureshi and N. A. Soomro and A. R. Memon},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215124881&doi=10.2118%2f222865-MS&partnerID=40&md5=32e8ddc777be342df8196b86a4eb7c60},

doi = {10.2118/222865-MS},

isbn = {978-195902549-8 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Soc. Pet. Eng. - ADIPEC},

publisher = {Society of Petroleum Engineers},

abstract = {The current revolution of generative artificial intelligence is transforming global dynamics which is also essential to petroleum engineers for effectively completing technical tasks.Henceforth the main aim of this study is to investigate the application of generative AI techniques for improving the efficiency of petroleum reservoir management.The outcomes of this study will help in developing and implementing generative AI algorithms tailored for reservoir management tasks, including reservoir modeling, production optimization, and decision support.In this study generative AI technique is employed to integrate with augmented reality (AR) to enhance reservoir management.The methodology involves developing a generative AI model to simulate pore-scale fluid flow, validated against experimental data.AR is utilized to visualize and interact with the simulation results in a real-time, immersive environment.The integration process includes data preprocessing, model training, and AR deployment.Performance metrics such as accuracy, computational efficiency, and user interaction quality are evaluated to assess the effectiveness of the proposed approach in transforming traditional reservoir management practices.The developed generative AI model demonstrated high accuracy in simulating pore-scale fluid flow, closely matching experimental data with a correlation coefficient of 0.95.The AR interface provided an intuitive visualization, significantly improving user comprehension and decision-making efficiency.Computational efficiency was enhanced by 40% compared to traditional methods, enabling real-time simulations and interactions.Moreover, it was observed that Users found the AR-driven approach more engaging and easier to understand, with a reported 30% increase in correct decision-making in reservoir management tasks.The integration of generative AI with AR allowed for dynamic adjustments and immediate feedback, which was particularly beneficial in complex scenarios requiring rapid analysis and response.Concludingly, the combination of generative AI and AR offers a transformative approach to reservoir management, enhancing both the accuracy of simulations and the effectiveness of user interactions.This methodology not only improves computational efficiency but also fosters better decision-making through immersive visualization.Future work will focus on refining the AI model and expanding the AR functionalities to cover a broader range of reservoir conditions and management strategies.This study introduces a novel integration of generative AI and augmented reality (AR) for reservoir management, offering a pioneering approach to pore-scale fluid flow simulation.By combining high-accuracy AI-driven simulations with real-time, immersive AR visualizations, this methodology significantly enhances user interaction and decision-making efficiency.This innovative framework transforms traditional practices, providing a more engaging, efficient, and accurate tool for managing complex reservoir systems. Copyright 2024, Society of Petroleum Engineers.},

keywords = {'current, AI techniques, Augmented Reality, Decision making, Decisions makings, Efficiency, Finance, Fluid flow simulation, Fluid-flow, Gasoline, High-accuracy, Management tasks, Petroleum refining, Petroleum reservoir evaluation, Pore scale, Real- time, User interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sahebnasi_exploring_2024,

title = {Exploring the Potential of Generative AI in Prototyping XR Applications},

author = {M. J. Sahebnasi and M. Farrokhimaleki and N. Wang and R. Zhao and F. Maurer},

editor = {Wang N. and Bellucci A. and Anthes C. and Daeijavad P. and Friedl-Knirsch J. and Maurer F. and Pointecker F. and Spano L.D.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196093148&partnerID=40&md5=a6264c6add18b5cd0ff99a0d3b25b822},

isbn = {16130073 (ISSN)},

year  = {2024},

date = {2024-01-01},

booktitle = {CEUR Workshop Proc.},

volume = {3704},

publisher = {CEUR-WS},

abstract = {This paper presents the initial stage of our research to develop a novel approach to streamline the prototyping of Extended Reality applications using generative AI models. We introduce a tool that leverages state-of-the-art generative AI techniques to facilitate the prototyping process, including 3D asset generation and scene composition. The tool allows users to verbally articulate their prototypes, which are then generated by an AI model. We aim to make the development of XR applications more efficient by empowering the designers to gather early feedback from users through rapidly developed prototypes. © 2024 Copyright for this paper by its authors.},

keywords = {AI techniques, Extended reality, generative artificial intelligence, Prototyping, Prototyping process, Scene composition, Software prototyping, State of the art},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{numan_spaceblender_2024,

title = {SpaceBlender: Creating Context-Rich Collaborative Spaces Through Generative 3D Scene Blending},

author = {N. Numan and S. Rajaram and B. T. Kumaravel and N. Marquardt and A. D. Wilson},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209252034&doi=10.1145%2f3654777.3676361&partnerID=40&md5=8744057832f9098eabfd16c8b2b5fe62},

doi = {10.1145/3654777.3676361},

isbn = {979-840070628-8 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {UIST - Proc. Annual ACM Symp. User Interface Softw. Technol.},

publisher = {Association for Computing Machinery, Inc},

abstract = {There is increased interest in using generative AI to create 3D spaces for Virtual Reality (VR) applications. However, today's models produce artificial environments, falling short of supporting collaborative tasks that benefit from incorporating the user's physical context. To generate environments that support VR telepresence, we introduce SpaceBlender, a novel pipeline that utilizes generative AI techniques to blend users' physical surroundings into unified virtual spaces. This pipeline transforms user-provided 2D images into context-rich 3D environments through an iterative process consisting of depth estimation, mesh alignment, and diffusion-based space completion guided by geometric priors and adaptive text prompts. In a preliminary within-subjects study, where 20 participants performed a collaborative VR affinity diagramming task in pairs, we compared SpaceBlender with a generic virtual environment and a state-of-the-art scene generation framework, evaluating its ability to create virtual spaces suitable for collaboration. Participants appreciated the enhanced familiarity and context provided by SpaceBlender but also noted complexities in the generative environments that could detract from task focus. Drawing on participant feedback, we propose directions for improving the pipeline and discuss the value and design of blended spaces for different scenarios. © 2024 ACM.},

keywords = {3D modeling, 3D scenes, 3D spaces, AI techniques, Artificial environments, Collaborative spaces, Collaborative tasks, Generative adversarial networks, Generative AI, Telepresence, Virtual environments, Virtual Reality, Virtual reality telepresence, Virtual spaces, VR telepresence},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vincent_roblox_2023,

title = {Roblox Generative AI in action},

author = {B. Vincent and K. Ayyar},

editor = {Spencer S.N.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167946022&doi=10.1145%2f3588430.3597250&partnerID=40&md5=61fda81c33eb3623240f7d14f51607b0},

doi = {10.1145/3588430.3597250},

isbn = {979-840070158-0 (ISBN)},

year  = {2023},

date = {2023-01-01},

booktitle = {Proc. - SIGGRAPH Real-Time Live!},

publisher = {Association for Computing Machinery, Inc},

abstract = {Roblox is investing in generative AI techniques to revolutionize the creation process on its platform. By leveraging natural language and other intuitive expressions of intent, creators can build interactive objects and scenes without complex modeling or coding. The use of AI image generation services and large language models aim to make creation faster and easier for every user on the platform. © 2023 Owner/Author.},

keywords = {AI techniques, Complex model, Creation process, Education, Game, Games, Interactive computer graphics, Interactive objects, Lighting, Metaverse, Metaverses, Modeling, Modeling languages, Natural languages, Object and scenes, Pipeline, Real-Time Rendering, Rendering (computer graphics)},

pubstate = {published},

tppubtype = {inproceedings}

}