Books¶
working
The references cited across this site, gathered in one place. These are the books worth returning to — not a reading list, but the shelf a page sends you to when its summary runs out.
Foundations¶
| Book | For |
|---|---|
| Trefethen & Bau, Numerical Linear Algebra | Conditioning and the SVD, exceptionally clearly |
| LeVeque, Finite Difference Methods for ODEs and PDEs | Stability, consistency, convergence |
| Saad, Iterative Methods for Sparse Linear Systems | Krylov methods, preconditioning |
| Briggs, Henson & McCormick, A Multigrid Tutorial | Short and genuinely tutorial |
| Bender & Orszag, Advanced Mathematical Methods | Asymptotics and matched expansions |
| Strang, Computational Science and Engineering | The broad survey |
| Wasserman, All of Statistics | Compact and rigorous |
| Gelman et al., Bayesian Data Analysis | The Bayesian reference |
| Smith, Uncertainty Quantification | Forward propagation, sensitivity, calibration |
| Oberkampf & Roy, Verification and Validation in Scientific Computing | The V&V standard |
| Roache, Verification and Validation in Computational Science | GCI and Richardson |
| Hairer, Lubich & Wanner, Geometric Numerical Integration | Symplectic theory |
| Martins & Ning, Engineering Design Optimization | Adjoints in a design context |
Fluids and aerospace¶
| Book | For |
|---|---|
| Batchelor, An Introduction to Fluid Dynamics | Conservation laws, constitutive relations |
| Ferziger, Perić & Street, Computational Methods for Fluid Dynamics | SIMPLE, PISO, Rhie–Chow |
| Moukalled, Mangani & Darwish, The Finite Volume Method in CFD | Unstructured detail, non-orthogonal correction |
| LeVeque, Finite Volume Methods for Hyperbolic Problems | Weak solutions, limiters, TVD |
| Toro, Riemann Solvers and Numerical Methods | The Riemann-solver reference |
| Hirsch, Numerical Computation of Internal and External Flows | Stability analysis, modified equations |
| Pope, Turbulent Flows | The graduate turbulence reference |
| Tennekes & Lumley, A First Course in Turbulence | Scales and the cascade, concisely |
| Wilcox, Turbulence Modeling for CFD | Model equations and their calibration |
| Anderson, Fundamentals of Aerodynamics / Introduction to Flight | The standard entry points |
| Katz & Plotkin, Low-Speed Aerodynamics | Panel methods |
| Etkin & Reid, Dynamics of Flight | Stability and control |
| Mattingly, Elements of Gas Turbine Propulsion | Cycle analysis |
| Sutton & Biblarz, Rocket Propulsion Elements | Rockets |
| Megson, Aircraft Structures for Engineering Students | Thin-walled structures |
| Hodges & Pierce, Structural Dynamics and Aeroelasticity | Flutter and divergence |
| Leishman, Principles of Helicopter Aerodynamics | Rotorcraft |
| Burton et al., Wind Energy Handbook | Turbines and plants |
| Vallado, Fundamentals of Astrodynamics and Applications | Orbits |
Meteorology and astrophysics¶
| Book | For |
|---|---|
| Wallace & Hobbs, Atmospheric Science | The survey |
| Holton & Hakim, An Introduction to Dynamic Meteorology | Dynamics |
| Vallis, Atmospheric and Oceanic Fluid Dynamics | The deeper treatment |
| Stull, An Introduction to Boundary Layer Meteorology | The ABL reference |
| Emanuel, Atmospheric Convection | Parcel theory done carefully |
| Petty, A First Course in Atmospheric Radiation | Radiation |
| Kalnay, Atmospheric Modeling, Data Assimilation and Predictability | NWP and DA together |
| Mihalas & Mihalas, Foundations of Radiation Hydrodynamics | Astrophysical radiation |
| Rybicki & Lightman, Radiative Processes in Astrophysics | The classic |
| Goedbloed & Poedts, Principles of Magnetohydrodynamics | MHD |
| Chen, Introduction to Plasma Physics | Plasma |
| Aarseth, Gravitational N-Body Simulations | N-body |
Machine learning and computing¶
| Book | For |
|---|---|
| Hastie, Tibshirani & Friedman, The Elements of Statistical Learning | The statistical foundation |
| Murphy, Probabilistic Machine Learning | The modern comprehensive text |
| Goodfellow, Bengio & Courville, Deep Learning | Still the reference for fundamentals |
| Rasmussen & Williams, Gaussian Processes for Machine Learning | GPs, free online |
| Brunton & Kutz, Data-Driven Science and Engineering | The SciML bridge |
| Kutz et al., Dynamic Mode Decomposition | DMD |
| Szeliski, Computer Vision | Free online, comprehensive |
| Hartley & Zisserman, Multiple View Geometry | Projective geometry |
| Hager & Wellein, HPC for Scientists and Engineers | Roofline and node-level performance |
| Kirk & Hwu, Programming Massively Parallel Processors | CUDA |
| Gorelick & Ozsvald, High Performance Python | Profiling and optimisation |
| Metcalf, Reid & Cohen, Modern Fortran Explained | Fortran as a live language |
| Tufte, The Visual Display of Quantitative Information | Figures |
| Hyndman & Athanasopoulos, Forecasting: Principles and Practice | Free online, the forecasting standard |
Connections¶
- Papers — individual references cited on specific pages.
- Videos and playlists — lecture series.
- Course archive — the offline material.