Heat Equation LLM Challenge Methodology Data Generation: Solved the heat equation using the Finite Difference Method (FDM) for Cases 1-4. Data Preprocessing: Extracted temperature distributions from VTK files and saved them in a structured CSV format. Fine-Tuning: Fine-tuned the IBM Granite 3.1 LLM using LoRA for parameter-efficient training. Evaluation: Generated responses to the questions in Questions.csv and created the submission.csv file. Results The fine-tuned model provided meaningful physical interpretations of the heat equation solutions. Responses were evaluated using cosine similarity against ground truth answers. Files heat_equation_llm.ipynb: Kaggle notebook with the full implementation. submission.csv: Submission file containing the model's responses. results_lora/: Directory containing fine-tuning results.