Výsledky vyhledávání - "Sai Sudhir, P."

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Multi-Knowledge Fusion Network for Time Series Representation Learning

Autor: Sakhinana, Sagar Srinivas, Gupta, Shivam, Aripirala, Krishna Sai Sudhir, Runkana, Venkataramana

Forecasting the behaviour of complex dynamical systems such as interconnected sensor networks characterized by high-dimensional multivariate time series(MTS) is of paramount importance for making informed decisions and planning for the future in a br

Externí odkaz: http://arxiv.org/abs/2408.12423

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Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning

Autor: Sakhinana, Sagar Srinivas, Aripirala, Krishna Sai Sudhir, Gupta, Shivam, Runkana, Venkataramana

Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time series(MTS) in interconnected sensor networks, is crucial for informed decision-making in various applications to minimize risk. Whil

Externí odkaz: http://arxiv.org/abs/2408.12409

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Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology

Autor: Sakhinana, Sagar Srinivas, Aripirala, Krishna Sai Sudhir, Gupta, Shivam, Runkana, Venkataramana

Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a range of intelligent functionalities, such as modeling, simulation, an

Externí odkaz: http://arxiv.org/abs/2408.12634

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PointSAGE: Mesh-independent superresolution approach to fluid flow predictions

Autor: Sarkar, Rajat, Aripirala, Krishna Sai Sudhir, Jadhav, Vishal Sudam, Sakhinana, Sagar Srinivas, Runkana, Venkataramana

Computational Fluid Dynamics (CFD) serves as a powerful tool for simulating fluid flow across diverse industries. High-resolution CFD simulations offer valuable insights into fluid behavior and flow patterns, aiding in optimizing design features or e

Externí odkaz: http://arxiv.org/abs/2404.04615

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