PHYS-665 Computational Modeling for Science and Engineering (3)

This course introduces computational modeling techniques essential for contemporary research in mathematics, computer science, physics, and related science and engineering disciplines. Students develop computational models that integrate mathematical formulation (e.g., writing governing equations and identifying relevant variables, parameters, and constraints), algorithmic design (e.g., numerical or stochastic, stability and convergence), domain-specific interpretation (e.g., interpreting outputs and validating results against theory or data), and related artificial intelligence(AI)/machine-learning methods (e.g., physics-informed neural networks and dimensionality reduction). Topics include numerical methods for ordinary and partial differential equations, optimization and inverse problems, Monte Carlo methods, data-driven modeling with AI and machine learning, and foundational concepts in high-performance and parallel computing. The course emphasizes model validation, interdisciplinary applications, and interpretation of computational and AI-enhanced results. Crosslist: PHYS-465.

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