Notes on control and machines that learn.
A working notebook at the edge where classical control theory meets modern machine learning — written for engineers who want the math, the code, and the intuition in the same place.
Topics
01 / ExploreControl Systems
PID, state-space, frequency domain, stability — the math that keeps things in line.
→Motor Control
DC, BLDC, stepper. FOC, commutation, encoders, current loops.
→Robotics
Path planning, motion control, manipulation, and the geometry of getting somewhere.
→Machine Learning
From linear regression to gradient boosting — the classical toolkit.
→Neural Networks
Architectures, training dynamics, and the bits underneath the hype.
→Reinforcement Learning
Agents, rewards, policies. Where control theory and ML meet.
→Tools
Linux, git, terminals, dotfiles — the practical tooling that makes engineering work.
→Stock Market
Markets from first principles — what moves prices, reading a company, portfolios, psychology, crashes, and forecasting.
→Recent writing
02 / Latest-
Jul 6, 2026Machine LearningWhy plain self-attention chokes on a million-point mesh (quadratic cost), and how Transolver's Physics-Attention fixes it: softly assign each mesh point to one of a small fixed ...
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Jul 6, 2026Machine LearningHow DoMINO (NVIDIA's Decomposable Multi-scale Iterative Neural Operator) predicts aerodynamics directly from a car's STL/point cloud. It encodes the whole geometry into a global...
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Jul 6, 2026Machine LearningWhy a plain 3D convnet on a car mesh is cubic (O(N^3)) and infeasible at high resolution, and how FIGConvUNet gets a global receptive field at quadratic (O(N^2)) cost: factorize...
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Jul 6, 2026Machine LearningPart I of a physics-ML monograph series on NVIDIA's simulation stack for vehicle engineering. What the PhysicsNeMo framework is, how its four repositories fit together, and a cu...
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Jul 6, 2026Machine LearningPart II of the physics-ML series. What it means for a simulator to have gradients, how NVIDIA Warp turns plain Python into GPU-speed physics code, how the Newton engine builds a...
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Jul 6, 2026Machine LearningPart III of the physics-ML series. Part I learned the physics; Part II differentiated it — this part is about everything around the physics: the PhysX engine that runs productio...
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Jul 6, 2026Control SystemsA plain-language field guide to Design and Process FMEA. Imagine how a design or process can fail, decide which failures matter, and fix the worst before a customer ever meets t...
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Jul 6, 2026Control SystemsHow a vehicle decides something is wrong, writes it down, lights a lamp, protects itself, and how that same machinery becomes the foundation for predicting failures before they ...