Výsledky vyhledávání

Graph Neural Networks (GNNs) have gained significant traction for simulating complex physical systems, with models like MeshGraphNet demonstrating strong performance on unstructured simulation meshes. However, these models face several limitations, i

Externí odkaz: http://arxiv.org/abs/2411.17164

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Object Recognition in Human Computer Interaction:- A Comparative Analysis

Autor: Ranade, Kaushik, Khule, Tanmay, More, Riddhi

Human-computer interaction (HCI) has been a widely researched area for many years, with continuous advancements in technology leading to the development of new techniques that change the way we interact with computers. With the recent advent of power

Externí odkaz: http://arxiv.org/abs/2411.04263

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The Design of an LLM-powered Unstructured Analytics System

Autor: Anderson, Eric, Fritz, Jonathan, Lee, Austin, Li, Bohou, Lindblad, Mark, Lindeman, Henry, Meyer, Alex, Parmar, Parth, Ranade, Tanvi, Shah, Mehul A., Sowell, Benjamin, Tecuci, Dan, Thapliyal, Vinayak, Welsh, Matt

LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and us

Externí odkaz: http://arxiv.org/abs/2409.00847

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A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations

Autor: Nidhan, Sheel, Jiang, Haoliang, Ghule, Lalit, Umphrey, Clancy, Ranade, Rishikesh, Pathak, Jay

In this paper, we propose a domain-decomposition-based deep learning (DL) framework, named transient-CoMLSim, for accurately modeling unsteady and nonlinear partial differential equations (PDEs). The framework consists of two key components: (a) a co

Externí odkaz: http://arxiv.org/abs/2408.14461

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