Physics

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.

A practical guide to numerical methods for the Cahn–Hilliard equation in alloy design—matrix and spectral schemes, quasi-steady reductions, and matrix exponentials—tested on spinodal benchmarks and illustrated with codes for binary and multicomponent systems.

The ultimate bridge from high school AP Physics to modern AI. Start with the familiar physics you know, translate it into Python, and use it as a launchpad to build advanced Machine Learning models and neural networks. Includes 100% solved exercises and click-to-run Google Colab labs.

Off Lagos, Greg is surrounded by the Atlantic and still cannot get three artificial intelligences to see the ocean. Somewhere else, the Council returns. Wave 4 goes looking for what everyone has been missing.

Quantum mechanics, the nucleus, and quantum computing — from what you already know as an engineer to a quantum computer you build in C#. Full rigour, honest limits, no hype. For the engineer who will model them.

Review the theory. Work through the examples. Solve the problems. Prepare for your examinations with confidence.Aerodynamics, Turbomachinery & Computational Fluid Dynamics is designed as a practical review and examination-preparation guide for engineering students. It brings together essential concepts, equations, methods, worked examples, solved problems, and numerical exercises to help students quickly revise and apply what they have learned.The book places particular emphasis on step-by-step worked examples and problem solving. Important formulas and principles are followed by applications showing how to approach typical engineering problems, perform calculations, and interpret the results.Use it for systematic revision throughout the semester or intensive preparation before examinations. The worked examples and problems are especially useful for developing speed, accuracy, and confidence in solving numerical and analytical questions.Ideal for students of aerospace engineering, mechanical engineering, aeronautical engineering, and related disciplines, preparing for university examinations, competitive examinations, and technical assessments.

First-principles lecture notes on flow in porous media: porosity, permeability, upscaling, and the mass, momentum, and energy balances behind Darcy's law. Currently spans 8 chapters. More chapters to be added in future versions.

How do complex phase-separation processes become solvable mathematical models?The Cahn–Hilliard equations lie at the heart of modern phase-field modeling—but their nonlinear, higher-order nature makes them notoriously challenging to solve.Solutions of the Cahn–Hilliard Equations explores analytical, approximate, and numerical approaches for tackling these equations, bringing together mathematical insight and practical solution techniques.A concise reference for researchers, engineers, and postgraduate students working in applied mathematics, computational science, materials modeling, and phase-field methods.Understand the equations. Explore the methods. Solve the problem.Rahul Basu

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.

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.