6 citations · 28 across the 40 of their papers we have counts for
42 papers
From 1D Benchmarks to Complex 3D Geometries: Can Agents Reliably Specify Problems for Automated PDE Simulation?
Akshat Jani, Sakhinana Sagar Srinivas, Venkataramana Runkana
Turning an engineering request into a simulation requires consistent geometry, physics, boundary conditions, and numerical settings. Recent agent-assisted systems automate simulati…
Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs
Prathamesh Gadekar, Sagar Srinivas Sakhinana, Venkataramana Runkana
Piping and Instrumentation Diagrams (P&IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what connects to what. Vision-langua…
Graph, Loop, and Harness Engineering for Zero-Trust Agentic Data Engineering and Analytical Processing
Sagar Srinivas Sakhinana, Venkataramana Runkana
Large language model agents increasingly automate data workflows, but end-to-end cloud data engineering and analytical execution require reliable coordination across code, data, in…
Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness
Sagar Srinivas Sakhinana, Venkataramana Runkana
Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and infrastructure, deploy services,…
Forward-Deployed Full-Stack Engineering for Autonomous Cloud MLOps
Sagar Srinivas Sakhinana, Venkataramana Runkana
Across industries, machine-learning systems support applications ranging from prediction and anomaly detection to forecasting, optimization, and scheduling, yet operationalizing th…
Beyond Pairwise Graphs in Science: Hypergraph Adaptive Wavelet Operators for Parametric PDEs
Rajat Sarkar, Venkataramana Runkana, Souvik Chakraborty
Physical systems are often modeled by solution operators that map input fields, parameters, geometries, or past states to steady or future physical states. Learning these maps is d…
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…
Multi Objective Design Optimization of Non Pneumatic Passenger Car Tires Using Finite Element Modeling, Machine Learning, and Particle swarm Optimization and Bayesian Optimization Algorithms
Priyankkumar Dhrangdhariya, Soumyadipta Maiti, Venkataramana Runkana
Non Pneumatic tires offer a promising alternative to pneumatic tires. However, their discontinuous spoke structures present challenges in stiffness tuning, durability, and high spe…
Thought-For-Food: Reasoning Chain Induced Food Visual Question Answering
Riddhi Jain, Manasi Patwardhan, Parijat Deshpande +1
The immense diversity in the culture and culinary of Indian cuisines calls attention to the major shortcoming of the existing Visual Question Answering(VQA) systems which are incli…
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…
Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis
Sakhinana Sagar Srinivas, Vijay Sri Vaikunth, Venkataramana Runkana
Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property…
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Geethan Sannidhi, Sreeja Gangasani +2
Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we int…
EMCNet : Graph-Nets for Electron Micrographs Classification
Sakhinana Sagar Srinivas, Rajat Kumar Sarkar, Venkataramana Runkana
Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is co…
Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection
Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
The current technology landscape lacks a foundational AI model for solving process engineering calculations. In this work, we introduce a novel autonomous agent framework leveragin…
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis
Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana
We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employ…
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…
Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering
Sagar Srinivas Sakhinana, Geethan Sannidhi, Venkataramana Runkana
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…
Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models
Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana
Characterizing materials with electron micrographs is a crucial task in fields such as semiconductors and quantum materials. The complex hierarchical structure of micrographs often…
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…