This book provides a comprehensive foundation in essential mathematical methods used in engineering and applied sciences. It bridges theory with practical applications by covering topics such as differential equations, vector calculus, Fourier analysis, and PDEs. Each chapter includes step-by-step explanations, solved problems, and practice exercises designed to build analytical thinking and problem-solving skills. Emphasis is placed on real-world engineering systems, making it an ideal guide for students, educators, and professionals.

AI and Complex Systems: Forecasting for Smarter Societal Decisions" explores the integration of artificial intelligence with complex systems to enhance decision-making in societal contexts. By leveraging predictive modeling and data analytics, AI helps to forecast outcomes, optimize strategies, and inform policy decisions. This approach enables smarter governance, more effective resource management, and better adaptation to evolving challenges, ultimately contributing to sustainable and informed societal development.

This book provides a comprehensive foundation in the mathematical tools essential for modern data science and machine learning. It blends core subjects such as linear algebra, calculus, probability, statistics, optimization, and numerical methods with real-world applications. Readers explore matrix operations, eigenvalues, and dimensionality reduction techniques like PCA and t-SNE. Optimization is covered through gradient-based methods and regularization strategies. Probability theory, Bayes' theorem, and statistical inference form the basis for modeling uncertainty. Information theory...

La modélisation spatio-temporelle des populations utilise des outils mathématiques et informatiques pour analyser l'évolution des populations dans le temps et l'espace. MATLAB offre un environnement flexible pour simuler ces dynamiques à l'aide d'équations différentielles, de modèles basés sur des agents et de cadres spatialement explicites. Ces modèles permettent d'étudier la distribution des espèces, leur migration et leurs interactions avec les facteurs environnementaux. Les composants clés comprennent les grilles spatiales, les méthodes de pas de temps et l'estimation des...

Bei der raumzeitlichen Bevölkerungsmodellierung werden mathematische und computergestützte Werkzeuge eingesetzt, um zu analysieren, wie sich Populationen über Zeit und Raum verändern. MATLAB bietet eine flexible Umgebung für die Simulation dieser Dynamik mithilfe von Differentialgleichungen, agentenbasierten Modellen und räumlich expliziten Rahmenwerken. Solche Modelle helfen bei der Untersuchung der Verteilung von Arten, der Migration und der Interaktion mit Umweltfaktoren. Zu den wichtigsten Komponenten gehören räumliche Gitter, Zeitschrittmethoden und Parameterschätzung. Die...

Spatiotemporal population modeling uses mathematical and computational tools to analyze how populations change over time and space. MATLAB provides a flexible environment for simulating these dynamics through differential equations, agent-based models, and spatially explicit frameworks. Such models help study species distribution, migration, and interactions with environmental factors. Key components include spatial grids, time-stepping methods, and parameter estimation. Applications range from ecology and epidemiology to urban planning. MATLAB's built-in functions and toolboxes allow for...