To affiliate with the cluster a student is required to take three core courses selected from each of the three core cluster areas. To earn a graduate certificate in Predictive Science and Engineering Design, a student must enroll in at least five approved courses (three core courses plus two electives).

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Core Cluster Areas (3 required courses)

PSED Seminar

PSED Seminar 510-1, 510-2
This is a literature and project combined seminar course focusing on the common principles and techniques underlying Predictive Science and Engineering Design (PSED). In addition to learning the fundamental principles and techniques associated with PSED, students will work in teams on interdisciplinary projects related to the current design focus of PSED.

Modeling, Simulation, and High Performance Computing

ChE 451 Applied Molecular Modeling
Introduction to modern, molecular-level, computational methods for calculating thermodynamic, transport, kinetic, and structural properties of molecules and materials.

CIV_ENG 426-1 or 2 Advanced Finite Element Methods, I or II (same as MECH_ENG 426-1 or 2 Computational Mechanics I or II)
Methods for treating material and geometric nonlinearities by finite elements; transient analysis: explicit and implicit time integration, partitioned methods, and stability; hybrid and mixed elements; finite elements for plates and shells; convergence, efficiency, and computer implementation.

EECS 358 Introduction to Parallel Computing
Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

EECS 467 Parallel and Distributed Database Systems
File allocation and load balancing in parallel I/O systems. Distributed, scalable file systems. Declustering and range partitioning. Parallel processing of relational queries: sort, clustering and join algorithms. Distributed database systems architectures. Query processing in distributed database systems: Processing simple queries; using semi-joins and joins for general queries.

IEMS 435 Introduction to Stochastic Simulation
Discrete event simulation modeling. Design and analysis of simulation experiments. Simulation programming in standard languages. Applications to manufacturing and services.

MSE 458 Computational Materials Science
Theory and application of atomic-scale computational materials tools to model, understand, and predict the properties of real materials.

MECH_ENG 470 High Performance Computing for Multiphysics Applications
This course will examine some of the theoretical and practical considerations that are important in developing and performing parallelized simulations of complex engineering systems.

MECH_ENG 417 or 418 Multisccale Modeling and Simulation in Mechanics, I or II
Learn theory and fundamentals principles of molecular simulation techniques nanomechanics and biomechanics. Apply molecular simulation tools to explain nanoscale solid and fluid mechanics phenomena with relevance to biological, bioinspired and organic materials. Learn multsicale simulation frameworks for investigating hierarchical systems.

CEE-ME 327 Finite Element Mthods in Mechanics
To learn a) the basic theory behind the finite element method (FEM), b) how to program the FEM using MATLAB, c) how to use a general commercial FEM code to solve practical engineering problems, and d) how to use data science techiques for the interpretation of the FEM solutions and in the solving mechanics of materials problems.

Computational Design Methods

BMD_ENG 384 Biomedical Computing
Principles of modern (computer-based) medical instrumentation, including analog-vs-digital design tradeoffs, efficient digital filter designs and algorithms for physiological signal processing, automated event recognition, and classification. Hardware and software design of microcomputer-based medical instruments.

IEMS 465 Simulation Experiment Design and Analysis
Point of error estimation, experiment design, run-length control, variance reduction, optimization via simulation, and input modeling for discrete-event stochastic simulation.

MAT_SCI 390 Materials Design
Analysis and control of microstructures. Quantitative process/structure/property/performance relations with case studies. Computer lab for modeling multicomponent thermodynamics and transformation kinetics.

MECH_ENG 341 Computational Methods for Engineering Design
Introduction to a wide range of computational techniques for engineering design. Modeling, simulation, optimization, design software, examples/projects with emphasis on computational techniques for design and manufacturing related applications.

MECH_ENG 395: Mechanistic Data Science for Engineering
We introduce mechanistic data science for engineering through the integration of scientific knowledge, such as physics and mechanics through six basic data science concepts: multimodal data generation and collection, feature engineering, dimension reduction, reduced order modeling, regression, and classification.

MECH_ENG 441-1 Engineering Optimization for Product Design and Manufacturing
Introduction to optimization theory and numerical techniques. Formulations, algorithms, computer implementation, examples/projects with emphasis in numerical and emerging techniques for design and manufacturing related applications.

COMP_SCI 349: Machine Learning
Machine Learning is the study of algorithms that improve automatically through experience. Topics covered typically include Bayesian Learning, Decision Tress, Genetic Algorithms, Neural Networks.

ELEC_ENG 375, 475: Machine Learning: Foundations, Applications, and Algorithms
The ultimate aim of the course is to equip you with all the modelling and optimization tools you'll need in order to formulate and solve problems of interest in a machine learning framework.

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Electives (2 required courses)

BMD_ENG 366 Biomechanics of Movement
Detailed analysis of human and animal movement. Modeling of muscle and tendon, kinematics of joints, and dynamics of multijoint movement. Applications of the theory to biomechanical problems in sports, rehabilitation, and orthopedics.

BMD_ENG 420 Biostatistics for Experimenters
Statistical methods for the design and analysis of experiments including randomization and blocking, Latin squares, factorial designs, sequential designs, analysis of variance, regression analysis, response surfaces, empirical and mechanistic model building.

IEMS 401 Intermediate Statistics
Linear model theory with application to multiple regression and analysis of variance. Statistical inference methods including likelihood estimation and testing, resampling and the Bayesian approach.

MECH_ENG 446 Advanced Tribology
Generalized Reynolds equation; thermal, turbulent, inertia, fluid compressibility, and surface roughness effects in sliding bearings; fatigue, scuffing, and wear in elastohydro-dynamic contact; plastohydrodynamic lubrication in metal rolling, extrusion, and forging.

MECH_ENG 451 Micromachining
Fundamental fabrication issues for microscale components used in MEMS/Nanotechnology. Understand and designing microfabrication processes based on photolithography and deposition/etching steps.

MECH_ENG 415: Mechanics of Manufacturing Processes
Understanding the fundamental mechanics of manufacturing processes is essential for process design, control, monitoring and innovation. This new course focuses on the mechanics of representative processes in the three fundamental processing categories: sheet metal forming in deformation-based processes, cutting in subtractive processes, and laser-engineered net shaping (LENS) in additive processes. The course discusses current theoretical and experimental advances in both fundamental process mechanics and technology advances and future developments.

MECH_ENG 495: Selected Topics: Introduction to Additive Manufacturing
This course is designed to provide an overview of available AM processes and basic scientific understanding of this emerging technology.

IEMS 304: Statistical Learning for Data Analysis
Predictive modeling of data using modern regression and classification methods. Multiple linear regression; logistic regression; pitfalls and diagnostics; nonparametric and nonlinear regression and classification such as trees, nearest neighbors, neural networks, and ensemble methods.

MAT_SCI 385: Electronic and Thermal Properties of Materials
Solid-state electronic structure from a solid-state chemistry perspective, phonons in complex materials, electronic and thermal transport at room temperature and above (semi-classical) of metals, semiconductors and some insulators.

MAT_SCI 358: Modelling and Simulation in Materials Science and Engineering
The course covers the essential methods and principles for modeling and simulating the structure, properties, and behavior of materials. It focuses on constructing models and identifying approaches to test either theoretical descriptions or experimental observations of materials phenomena on a computer.

COMP_SCI 396, 496: Deep Learning
In this course students will study deep learning architectures such as autoencoders, , convolutional deep neural networks, and recurrent neural networks. They will read original research papers that describe the algorithms and how they have been applied to fields like computer vision, automatic speech recognition, and audio event recognition.

ES_APPM 448: Numerical Methods for Random Processes
Analysis and implementation of numerical methods for random processes: random number generators, Monte Carlo methods, Markov chains, stochastic differential equations, and applications.