Neural Networks

Where neural operators fit vehicle health: as fast surrogates for the expensive physics simulations — battery thermal fields, electrochemical state, structural response — that a...

Neural operators learn mappings between functions, not just numbers — so one trained model can solve a whole family of physics problems at once, and generalize across resolution...

Where graph neural networks fit vehicle health monitoring. A battery pack of cells, a powertrain of coupled components, and a fleet of related vehicles are all graphs — and lear...

A single, complete introduction to graph neural networks, in two parts. Part 1 (explained simply) walks the anatomy from the ground up — the input encoder, the stack of message-...

A reading map for the estimation-and-learning thread — physics-informed neural networks, classical Kalman and particle filters, and diffusion models — and how they connect for e...

How diffusion models turn noise into structure. Add noise to data until nothing is left, then train a network to undo it one small step at a time; sampling runs that reversal to...

The classical estimators that track a hidden state from noisy measurements. How the Kalman filter alternates predict and correct to fuse a model with data, why the extended and ...

Where physics-informed neural networks earn their keep in vehicle health monitoring. Battery state-of-charge and state-of-health estimation, thermal and electrochemical field re...

A single, complete introduction to physics-informed neural networks, in two parts. Part 1 (in plain words) builds the idea from the ground up — the mass-on-a-spring intuition, h...

Attention is the one mechanism that pulled neural networks out of recurrent dead-ends and into the modern era. Every modern language model — GPT, Claude, Gemini, Llama — is some...

An interactive walkthrough of the thirteen ideas every neural-network practitioner reaches for — dataset splits, signal preprocessing, the bias–variance trade-off, gradient desc...

The long-form reference behind the interactive neural-networks primer. Eighteen entries — train/val/test splits, preprocessing pipelines, Savitzky–Golay smoothing, outlier metho...