6 citations · 21 across the 29 of their papers we have counts for
16 papers · 1 filter
PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference
Akshat Jani, Prathamesh Gadekar, Sakhinana Sagar Srinivas +1
We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The workflow links problem specific…
Scaling Test-Time Inference with Policy-Optimized, Dynamic Retrieval-Augmented Generation via KV Caching and Decoding
Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta +1
We present a comprehensive framework for enhancing Retrieval-Augmented Generation (RAG) systems through dynamic retrieval strategies and reinforcement fine-tuning. This approach si…
AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up
Sakhinana Sagar Srinivas, Shivam Gupta, Venkataramana Runkana
Recent advances in generative AI have accelerated the discovery of novel chemicals and materials. However, scaling these discoveries to industrial production remains a major bottle…
Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework
Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta +1
The growing use of foundation models (FMs) in real-world applications demands adaptive, reliable, and efficient strategies for dynamic markets. In the chemical industry, AI-discove…
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta +1
Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of preci…
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device techno…
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning
Sakhinana Sagar Srinivas, Venkataramana Runkana
In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design a…
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
Sakhinana Sagar Srinivas, Chidaksh Ravuru, Geethan Sannidhi +1
Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by…
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Sagar Srinivas Sakhinana, Geethan Sannidhi, Chidaksh Ravuru +1
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynam…
Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving
Sakhinana Sagar Srinivas, Vijay Sri Vaikunth, Venkataramana Runkana
We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework empl…
Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology
Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta +1
Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a…
Multi-Knowledge Fusion Network for Time Series Representation Learning
Sagar Srinivas Sakhinana, Shivam Gupta, Krishna Sai Sudhir Aripirala +1
Forecasting the behaviour of complex dynamical systems such as interconnected sensor networks characterized by high-dimensional multivariate time series(MTS) is of paramount import…
Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning
Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta +1
Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time series(MTS) in interconnected sensor networks, is crucial for i…
Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Anomaly detection is fundamental yet, challenging problem with practical applications in industry. The current approaches neglect the higher-order dependencies within the networks…
Vision HgNN: An Electron-Micrograph is Worth Hypergraph of Hypernodes
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Sreeja Gangasani +1
Material characterization using electron micrographs is a crucial but challenging task with applications in various fields, such as semiconductors, quantum materials, batteries, et…
Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The ex…