Numerical Methods with Python and MATLAB: A Practical Guide for Engineers and Scientists

By: Desai, Payal S.

Learn numerical methods the practical way: understand each method, do one step by hand, automate it in Python and MATLAB, and check the answer.

Equations that cannot be rearranged, tables of measurements, systems with thousands of unknowns, floods, vibrating buildings and spreading epidemics: this textbook shows engineers and scientists how to turn real problems into reliable numbers. Every method is introduced with a real-world problem and a clear figure, worked by hand on a small example, and then coded in short, readable programs given side by side in Python (NumPy, SciPy, Matplotlib) and MATLAB. Every program was run and both versions were checked against each other.

Topics coveredProgramming basics in Python and MATLAB, round-off and truncation errorRoots of equations: bisection, Newton-Raphson, secant, systems of nonlinear equationsLinear systems: Gauss elimination, LU decomposition, Jacobi and Gauss-Seidel; eigenvalues and eigenvectorsInterpolation, cubic splines, curve fitting, least-squares regression and nonlinear fittingNumerical differentiation and integration: trapezoidal, Simpson, Romberg, Gauss quadrature, adaptive methodsOrdinary differential equations: Euler, Heun, Runge-Kutta (RK4), adaptive steps and stiff problemsBoundary-value problems and partial differential equations: shooting, finite differences, heat, Laplace and wave equationsOptimization: golden-section search, steepest descent, Newton's method and linear programming (simplex)Eight case studies: flood routing, water-supply networks, earthquake response, rail track, river pollution, drug dosing, epidemic modelling and planetary energy balanceInside the book116 worked examples, each with Given/Find, hand steps, code, output, an Answer box and a CHECK box172 graded practice problems with answersA reusable library of 55 functions you build yourself, in both languages"Where does it come from?" derivations with figures, key-formula boxes, "when to use / when it fails" guidance and common mistakes in every chapterExamples drawn equally from engineering and from science and everyday lifeAppendices: Python-MATLAB quick reference, linear algebra and calculus review, complete library listing, installation guide

Ideal for undergraduate courses in civil, mechanical, chemical, electrical and environmental engineering, physics, chemistry and biology, and for professionals who want a practical, code-ready reference. Only first-year calculus is assumed, and no previous programming experience is needed.

• Format: Paperback
• Language: English
• ISBN: B0HNRFTNYQ
• ISBN13: 9798164031909
• Release Date: October 2026
• Publisher: Independently Published
• Length: 516 Pages
• Weight: 1.50 lbs.
• Dimensions: 1.0" x 6.0" x 9.0"