Latest Blog Posts

• Published on

  February 19, 2026

  

  Physics-Informed Neural Networks: What, Why, and How

  Generative-AIInterpretabilityScientific-Computing

  Physics-Informed Neural Networks (PINNs) are a groundbreaking class of models that merge these two worlds, creating neural networks that are constrained by the laws of physics. This article explores how PINNs work, why they are revolutionizing scientific computing, and what their future holds.

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• Published on

  February 9, 2026

  

  Interpreting Vision Models with Grad-CAM and Attention Maps

  </>Computer-VisionInterpretabilityViT

  Why did my model classify this image as a 'cat'? This article dives deep into two key visualization techniques—Grad-CAM for CNNs and Attention Maps for Transformers—to answer that question. We explore the mechanics of how these maps are made, what influences them, and how they serve as powerful tools for debugging model biases and testing robustness.

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• Published on

  January 29, 2026

  

  An Introduction to Explainable AI Methods

  AI-SecurityInterpretability

  Deep learning models are often 'black boxes,' making them difficult to trust. This article introduces the field of Explainable AI (XAI) and provides a comprehensive overview of five key post-hoc explanation methods: Occlusion Analysis, Saliency Maps, Grad-CAM, LIME, and SHAP. We explore how each technique works and its role in making AI more transparent.

  6 min read

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