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of 11
pro vyhledávání: '"Sakhinana, Sagar Srinivas"'
The current technology landscape lacks a foundational AI model for solving process engineering calculations. In this work, we introduce a novel autonomous agent framework leveraging Retrieval-Augmented Instruction-Tuning (RAIT) to enhance open, custo
Externí odkaz:
http://arxiv.org/abs/2408.15866
In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI, such as Large Multimodal Models (LMMs)
Externí odkaz:
http://arxiv.org/abs/2409.00082
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynamic, multi-modal approach that integrates the strengths of traditional
Externí odkaz:
http://arxiv.org/abs/2408.13622
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
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
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
Time series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal dependencies and distribution shifts in learning from historical context to predict task-specific outcomes. To address these challenge
Externí odkaz:
http://arxiv.org/abs/2408.14484
Pre-trained large language models (PLLMs) like OpenAI ChatGPT and Google Gemini face challenges such as inaccurate factual recall, hallucinations, biases, and future data leakage for temporal Knowledge Graph (tKG) forecasting. To address these issues
Externí odkaz:
http://arxiv.org/abs/2408.13273
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
Autor:
Sarkar, Rajat Kumar, Majumdar, Ritam, Jadhav, Vishal, Sakhinana, Sagar Srinivas, Runkana, Venkataramana
In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations poses a significant challenge due to the fundamental contrast between
Externí odkaz:
http://arxiv.org/abs/2311.09740