THE COURSES I OFFER AT PENN STATE
ESC 407: Numerical Methods in Engineering Design - Spring
The Numerical Methods in Engineering Design course for undergraduates covers fundamental numerical algorithms and introduces a range of machine learning tools. Students learn techniques such as numerical integration, root-finding, and optimization, and gain hands-on experience with machine learning models like Support Vector Machines (SVM) and neural networks using TensorFlow II and PyTorch. This course equips students with essential skills to tackle complex engineering problems through both traditional and data-driven approaches.
EMCH 315: Mechanical Response of Engineering Materials - Spring
The Mechanical Response of Engineering Materials course for undergraduates explores the behavior of materials under various loading conditions. Students study key concepts such as stress, strain, and deformation, and examine material properties through experiments and theoretical models. The course includes practical applications in material testing and analysis, preparing students to understand and predict material performance in real-world engineering scenarios.
EMCH 560: Finite Elements - Fall
The Finite Elements course for graduate students delves into the foundational principles of finite element analysis and its applications in engineering. The course covers essential topics such as the discretization of partial differential equations and the implementation of finite element models. A dedicated module focuses on integrating neural networks with finite element methods to develop surrogate models and apply advanced data science techniques, enhancing the accuracy and efficiency of simulations and predictions.
The Numerical Methods in Engineering Design course for undergraduates covers fundamental numerical algorithms and introduces a range of machine learning tools. Students learn techniques such as numerical integration, root-finding, and optimization, and gain hands-on experience with machine learning models like Support Vector Machines (SVM) and neural networks using TensorFlow II and PyTorch. This course equips students with essential skills to tackle complex engineering problems through both traditional and data-driven approaches.
EMCH 315: Mechanical Response of Engineering Materials - Spring
The Mechanical Response of Engineering Materials course for undergraduates explores the behavior of materials under various loading conditions. Students study key concepts such as stress, strain, and deformation, and examine material properties through experiments and theoretical models. The course includes practical applications in material testing and analysis, preparing students to understand and predict material performance in real-world engineering scenarios.
EMCH 560: Finite Elements - Fall
The Finite Elements course for graduate students delves into the foundational principles of finite element analysis and its applications in engineering. The course covers essential topics such as the discretization of partial differential equations and the implementation of finite element models. A dedicated module focuses on integrating neural networks with finite element methods to develop surrogate models and apply advanced data science techniques, enhancing the accuracy and efficiency of simulations and predictions.
