Physics‑Informed Neural Network for Single Particle Model SOC and SOH Prediction
A Physics‑Informed Neural Network (PINN) is a neural network trained not only on data but also on physical laws, typically expressed as differential equations.
A Physics‑Informed Neural Network (PINN) is a neural network trained not only on data but also on physical laws, typically expressed as differential equations.
A Retrieval‑Augmented Generation (RAG) system that ingests documents, creates embeddings, stores them in a vector database, and retrieves relevant context to generate accurate, grounded AI responses using LLMs for search, chat, and knowledge automation.
A fully automated RAG AI agent that ingests new Google Drive files, generates embeddings, stores them in Pinecone, and uses an AI Agent with memory and retrieval to answer chat queries using OpenAI models for accurate, context‑aware responses.
Build an agentic RAG application from scratch by collaborating with Claude Code.
An AI‑powered multi‑action assistant built in n8n that sends emails, fetches real‑time news, and creates meeting invitations from natural language commands. The AI Agent interprets each message, selects the right tool, and executes tasks automatically through Gmail, News API, and Google Calendar.
A workflow that connects WhatsApp with an AI agent using n8n. Incoming messages are processed by an LLM, which generates intelligent responses and sends them back automatically through WhatsApp.
An n8n workflow that reads top AI news from RSS feeds, uses an AI agent to extract and transform the raw articles into clear, readable summaries, and sends the final insights directly to WhatsApp. This creates an automated, real‑time AI news delivery system. 
An automated n8n workflow that fetches top HackerNews stories, extracts raw data, and uses an AI agent to generate clean, readable summaries. The processed insights are sent directly to WhatsApp via the WhatsApp Cloud API, creating a seamless, automated tech‑news delivery system.