Notes on machine learning, digital twins, optimization, and scientific AI.
From statistical forecasting to foundation models — how time series AI is evolving from predicting signals to understanding system dynamics.
ML / EngineeringHow retrieval-augmented generation is evolving to handle images, tables, and multi-modal scientific data.
LLMsFoundation models for scientific discovery — architecture choices, pre-training strategies, and downstream tasks.
ML / EngineeringA technical analysis of how LLMs are reshaping engineering workflows, design, and simulation.
LLMsDefining digital twins, their architecture, and real-world applications in manufacturing and engineering.
Digital TwinsUsing ThreadPoolExecutor and as_completed for high-performance parallel workloads in Python.
ML / EngineeringHow ML supercharges digital twins — from predictive maintenance to autonomous optimization loops.
Digital TwinsApplying prompt engineering techniques to leverage LLMs in the engineering design process.
LLMs