Dr. Preston's
Physics & Engineering Bookshelf

The clearest QM textbook written in English. Griffiths has a gift for building intuition before formalism — I returned to this book every time a derivation in a paper stopped making sense. The 2018 edition adds a strong chapter on entanglement. If you're doing a physics degree or AFOQT physics prep, this is book one.

Where Griffiths builds intuition, Bransden gives you the full formal scaffolding. I used this at AFIT when the research problems required rigorous treatment of scattering theory and nuclear matrix elements. Denser than Griffiths but thorough — graduate-level QM done properly.

Townsend starts from spin-1/2 systems rather than wave mechanics — a genuinely different pedagogical angle that snapped certain things into focus for me that Griffiths left fuzzy. Recommended as a second pass through undergraduate QM, especially if you're planning graduate work.

The MIT series book for QM — pedagogically brilliant, historically motivated, and full of worked problems that build genuine physical intuition. I read this before Griffiths and it made the whole subject click. The experiments-first approach is especially valuable for anyone with a laboratory background.

Purcell's classic, updated by Morin with SI units and a wealth of new problems. The physical reasoning in this book is unmatched — Purcell derives magnetism from special relativity in a way that permanently changes how you think about fields. Essential for anyone who wants to understand E&M rather than just solve it.

The standard undergraduate E&M textbook for a reason — Griffiths writes with extraordinary clarity and every chapter builds directly on the last. I used the 4th edition for Berkeley coursework and kept it on my desk throughout grad school as a reference. The problem sets alone are worth the price of admission.

The graduate-level EM text for engineers — radiation, waveguides, antennas, and scattering treated with full mathematical rigor. This was required reading for the applied electromagnetics component of my PhD work. Heavy going, but if you need to understand how EM energy propagates through matter, Balanis is the reference.

The definitive undergraduate nuclear physics text — radioactive decay, nuclear reactions, fission, and detector physics all treated clearly with solid problem sets. I used Krane at Berkeley and again in my first semester at AFIT. It bridges the gap between quantum mechanics and practical nuclear engineering better than anything else at this level.

If you work in nuclear engineering, you will use Knoll. It is the complete treatment of how radiation interacts with matter and how detectors work — from gas-filled counters to scintillators to semiconductor devices. My PhD research involved gadolinium cross-section measurements and this book was open on my bench continuously throughout that work.

The graduate-level reactor theory text. Diffusion theory, neutron transport, criticality calculations, and reactor kinetics — all derived from first principles with enough rigor to build real intuition. Dense but rewarding. If you're doing any computational neutronics work, this is the foundation you need before touching a code like MCNP.

The clearest health physics textbook I've encountered — Turner builds from atomic structure through interaction physics to dosimetry with exceptional pedagogical care. This was my primary reference for the radiation interaction physics component of my coursework and is approachable enough for motivated undergrads preparing for grad school.

The standard graduate text for nuclear chemistry and radiochemistry — covers nuclide production, separation techniques, isotope tracers, and reactor chemistry. Indispensable for anyone working in the fuel cycle, waste management, or isotope production space. My go-to when the chemistry questions in my research crossed into nuclear territory.

A rigorous but accessible survey of nuclear power from physics through policy — reactor types, waste management, proliferation risk, and economics all treated honestly. I used this text when teaching and found it invaluable for students who needed the full picture without the textbook weight of Duderstadt. Strong on context that pure engineering texts ignore.

The encyclopedic reference for mathematical physics — Green's functions, complex analysis, special functions, group theory, and everything in between. I used individual chapters as primary references throughout my PhD when a derivation required a mathematical technique I hadn't seen before. Dense, but the depth is the point. Worth having on the shelf throughout your career.

The engineering mathematics bible — ODEs, PDEs, linear algebra, complex analysis, numerical methods, and probability all in one volume. I kept a copy at Berkeley and a second at AFIT. Clear explanations, exhaustive problem sets, and a sensible organization make this the book to reach for when you need to refresh a technique fast.

The most widely adopted calculus textbook in the US for good reason — clear structure, excellent visual explanations, and enough practice problems to build genuine fluency. I used this throughout Berkeley. If you are preparing for calculus-heavy standardized tests or returning to technical coursework after a gap, Stewart is your starting point.

The best practical ML book available. Géron teaches by building — every concept is immediately demonstrated in working code, and the progression from classical ML through deep learning through deployment is the clearest I've seen in any single volume. I assigned chapters from this during lab meetings. The 3rd edition added transformers and diffusion models.

Chollet wrote Keras — and this book reflects that depth of understanding. Where Géron is comprehensive, Chollet is precise: you understand not just how to use the API but why the architecture decisions were made. The second edition adds transformers and best practices that weren't in the 2017 original. I used both editions in succession.

This book is specifically for scientists and engineers who want to understand machine learning from first principles rather than treating it as a black box. Brunton bridges dynamical systems, SVD, sparse regression, neural networks, and control — the kind of ML that actually makes sense in physics research. Exceptional for someone with a technical background approaching ML seriously.

The accessible gateway to statistical learning — linear models through SVMs, trees, and unsupervised methods, all with R and Python code. I used this when I needed to move quickly through classical ML and understand what the deep learning methods were replacing. Free PDF from the authors makes it a no-brainer as a starting reference.

The mathematical foundation that most ML courses skip — linear algebra, multivariate calculus, probability, and optimization all built from scratch with ML applications as motivation throughout. I used this to fill gaps from physics-track math courses when the research moved into probabilistic graphical models. Free PDF available from the authors.

Reif treats thermal and statistical physics as a unified subject — which is the right way to teach it — and does so with exceptional rigor. Every result is derived from first principles rather than asserted. Berkeley used this text and the derivations genuinely improved my intuition for reactor thermodynamics later in my career. The problem sets are hard and that's the point.

More approachable than Reif while covering the same ground — the Blundell text is excellent for self-study and as a complement to a more formal treatment. The applications sections (cosmology, astrophysics, biological systems) make the physics feel alive rather than abstract. I used this as a second pass through material that Reif had made dense.

A practical, no-fluff collection of ready-to-use AI prompts for educators — covering lesson planning, student communication, assessment design, and administrative tasks. If you're a teacher or tutor who wants to save time and get more out of tools like ChatGPT and Claude, this book gives you a working toolkit without the learning curve. Useful for tutors, coaches, and anyone building educational content.