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AI-Teaching Assistant
- Developed scripts to scrape structured data from the textbook/ lectures using OpenAI API to obtain training Question-Answer pairs.
- Fine-tuned Llama 3.2-11B using LoRA to develop a domain specific expert. Carried out hyperparameter optimization using Optuna and WANDB.
- Developed Retrieval-Augmented Generation (RAG) based on several embedding model’s to leverage the fine-tuned multimodal Large Language model (LLM).
- Aimed at improving education with providing a specialized AI assistant for targeted academic support.
- Demo AI-TA available here. Paper submission to a reputed conference is in progress.
Vision Transformer to learn physics
- Applying Vision Transformers (ViT) to learn physics from simulation images.
- Using generative AI to learn coupled emergent spatiotemporal multiphysics phenomena.
- Link to GitHub repo.
Multimodal transformers
- Developing a Vision Language model (VLM) to explain the physical simulations and its mathematics.
- Integrating fine-tuned Llama 3.2-11B for language processing with a vision transformer coupled via cross attention between LLaMA embeddings and visual tokens.
Multiphysics modeling using Finite Element method (FEM)
- Modeling of emergent phenomena involving multiscale/multiphysics interactions using advanced numerical techniques and high-performance scientific computing.
- Developing phenomenological description of the constituent processes, numerical formulation of the governing equations
- Developing computational strategies and parallel code infrastructure for handling multi-million degrees of freedom
- The code framework is in C++ and uses open source libraries like dealii (FE library), PETSc, Trilinos etc.
- Applications include solid-state battery, shell, neurons.
Physics informed neural network (PINNs)
- Used PINNs to approximate the solution of a general type of partial differential equations.
- Implemented using PyTorch and compared the results with numerical solvers based solution.
