Physics

Build a strong foundation in physics without getting lost in jargon or formulas. Atom Education: Physics 101 combines intuitive explanations, practical examples, and structured problem-solving to make essential physics concepts easier to understand.

Detailed Class Notes on High School AP Calculus - Take it FREE or 100% of what you pay goes to Oxfam America.

Quantum mechanics, general relativity, and the Standard Model are all exactly right in their domains. This book shows they are scale-regime corners of a single master equation, derived using experiments already performed, constants already measured, and the structural requirement that equations for physically-real potentials be self-consistent. v4.0 integrates the dark-matter sector: the substrate's neutrino structure forces a rank-three mass matrix with one exactly massless active eigenstate and a vanishing neutrinoless-double-beta-decay rate, and the heavy mostly-sterile eigenstate reads as a conditional cold-dark-matter candidate at ~10^12 GeV. This edition also reframes the Clay Yang-Mills mass-gap question on the substrate ontology and displays the closed form of the metric response through Lovelock's theorem.

Ship the NEXUS-1 backend for real: a genuine SQL Server on every test run, a dispatcher proven under a killed process, structured logs with a correlation id, and a CI gate that will not package a red build. Two hundred tests, one Dockerfile, one honest boundary.

From Flow to Proof turns distributed-system promises into explicit models, properties, counterexamples, and evidence. Explore state machines, contracts, temporal logic, concurrency, ownership, refinement, and honest proof boundaries—with companion artifacts at github.com/gregory82gr/Nexus-1-phase-0.

Give the tested core a database and a public API — without letting either touch its rules. Build NEXUS-1's Infrastructure and API layers in .NET: EF Core, repositories, the outbox, RFC 7807, JWT. 654 tables, one seam that stays signed — every rule proven by watching it fail.

Build microservices the way architects do—not by splitting code, but by discovering true boundaries. Using the NEXUS-1 digital twin, this book explores DDD, APIs, messaging, eventual consistency, sagas, resilience, and migration, showing when distributed systems are worth their cost.

Advanced DDD before microservices: From Context to Flow shows how NEXUS-1 moves from bounded contexts to domain events, integration events, outbox, sagas, anti-corruption layers, auditability, and eventual consistency.

From Queue to Core reveals the stochastic foundation behind reactor kinetics: birth–death chains, master equations, delayed neutrons, feedback, deterministic point kinetics, and C# algorithms — all explained through the NEXUS-1 demonstrator with clear boundaries and interpretable results.

A .NET architecture book that proves what it teaches: build NEXUS-1's Domain and Application layers with Clean Architecture, DDD, and CQRS — zero database, zero web server, fifty green tests. Every rule watched failing on purpose, every layer diagrammed in UML. Nineteen chapters, nineteen Deep Dives, one honestly bounded software core.

Two builds, one schema, zero faith required. From Table to Twin walks an industrial digital twin's SQL Server and EF Core backbone by hand and from code, then prints exactly where Database First and Code First disagree — for .NET developers who check every table against a live, running console.

Learn Domain-Driven Design from zero in plain language, then see it applied to NEXUS-1: a digital-twin architecture with signals, alarms, root-cause cases, policies, audit, SQL, and EF Core. A practical guide for .NET developers who want clearer language, boundaries, rules, and meaning in complex systems.

We live inside Time: sometimes wishing it would pass faster, sometimes praying for more, and often trying to conquer it. Wave 2 proposes a different quest: to look more deeply at Time’s nature, so we may better understand who we are, where we belong, and how our becoming may find its purpose.

Special relativity is a fascinating subject that changes how you see a torch. These course notes will help guide you through all of the content, without the overhead that textbooks have. Straight, clean and simple.

A practical, code-first guide to physics-informed machine learning for scientists and engineers. You'll build a working Physics-Informed Neural Network from scratch in PyTorch, solve a real differential equation, and verify it against the exact solution — staying honest about both the power and the limits of the method.