Machine Learning

Despite impressive advances in machine learning, recent claims that AI can replace physical laws are deeply flawed. Machine learning models often just replicate existing knowledge from data generated by known physics, lacking true understanding or causal reasoning. While AI is a powerful tool for accelerating research and automating tasks, genuine scientific discovery and the formulation of physical laws still require human insight and expertise.

A new framework for neural processing of dynamic stimuli

A playful rebranding of machine learning jargon with a physics twist—plus a shoutout to Geoff Hinton’s historic achievements!

This blog offers a comprehensive technical and philosophical exploration of Kalman filtering, focusing on its implementation and reinterpretation within the PyTorch ecosystem. I will explore the mathematical foundations, practical coding strategies, differentiable filtering, and the deeper questions about knowledge, uncertainty, and learning that arise when classical estimation theory meets modern neural computation.

Computational Pipeline for Large-Scale Histology

End-to-end pipeline for mult-scale modelling

Statistical Finite Element Analysis

Computational Pipeline for Large-Scale Histology

Normalizing Flows might not be the flashiest generative models, but their ability to provide exact, normalized densities makes them invaluable for tasks like variational inference. The new ‘Tensorizing Flows’ approach upgrades NFs with a powerful tensor-train base, making them far better at handling complex, high-dimensional, and multimodal distributions—especially in scientific and physics applications.