Courses

This graduate course in modern automotive design covers concepts in: prime movers, aerodynamics, brakes, tires, steering, transmissions, suspension and handling, chassis, and advanced hybrid powertrains. Simulations and physical prototyping give students a hands-on approach to the design, optimization, fabrication, and testing of various vehicle subsystems in a team-based learning environment. Projects are based in the competitive automotive teams based in the MEMS department—Duke Motorsports (Duke FSAE) and Duke Electric Vehicles (DEV).

Instructor: George Delagrammatikas

This course delves into the carbon cycle and strategies for managing greenhouse gases to combat climate change. The curriculum focuses on the techniques for measuring, monitoring, and reducing carbon emissions, introducing students to the science of carbon management as practiced by various industries. By aligning with Duke’s Climate Commitment, the course aims to equip students with the skills to develop and implement climate solutions in a timely, effective, and fair manner. This course is expected to prepare students for the clean energy industry and equip students with essential skills and knowledge to support the energy transition and align with the U.S. 2050 vision for a sustainable, carbon-neutral future.

Instructor: Liang Feng

This course connects key ethics principles of trust, bias/equity, surveillance/privacy and safety with real life case studies that consider technical options/opportunities, policy effects, and regulatory efficacy. The goal of this course is for students to develop a deeper understanding of how ethics applies to the decisions they will make as engineers developing robotic tools and autonomous systems. Additionally, this course sheds light on how companies and regulators work in different contexts in the United States to effectively manage the emergence of autonomy in our hospitals, highways, and many other areas of our lives.

Instructor: Siobhan Oca

In these special topics courses, Data Science and AI principles will be applied to materials science research projects conducted by interdisciplinary teams. The course occurs over two semesters; Part 1 in the Fall (2 credits) is followed by Part 2 in the Spring (1 credit). Students will attend a series of seminars and research, develop, and carry out a project incorporating elements from machine learning and materials science. This course is designed for advanced graduate students with a background in data science, machine learning, and materials science. This course is one of four courses that make up the AI for Materials Science (aiM) certificate.

Instructors: Shana Lee McAlexander and Richard Sheridan

This course connects the fields of dynamical systems and control with emerging topics in machine learning and data science. In addition to discussing relevant works from research articles, the course content will mostly follow the topics described in a recently published book to introduce fundamental topics.

Instructor: Brian Mann

This course equips students with the tools to analyze and interpret large datasets using Python. The course covers supervised methods like logistic regression, support vector machines, and decision trees, as well as unsupervised techniques such as k-means clustering and PCA. Students will also be introduced to deep learning, including convolutional neural networks and contemporary language models. Hands-on coding assignments, quizzes, exams, and a final project ensure practical understanding and application of these machine learning concepts.

Instructor: Jonathan R. Holt

Nature (with engineering in it) is design in motion, constant change, freedom, evolution, in harmony. The course teaches: –how to predict efficient and long-lasting design everywhere (shape, structure, size, rhythm, performance), such as power plant evolution, animal design evolution, city design evolution, trees and forests, rivers and deltas, pedestrian evacuation paths during disaster, etc. The list is wide open.—how to fast-forward design evolution, on the back of the envelope. —the art of questioning, and the power of the science of form (flow, configuration, evolution) as a counterweight to the doctrine of reductionism, invisible stuff, jargon, and handwaving. —the meaning of the words used in university education—theory, as a counterweight to empiricism—holism and student purpose, i.e., how the courses taken piecemeal (as disciplines) fit together.

Instructor: Adrian Bejan

This course provides a unique opportunity to examine various engineering technologies and applications in urology for the treatment of kidney stone patients and other benign urinary tract diseases. Selective topics in laser lithotripsy, shock wave lithotripsy, high-intensity focused ultrasound and ultrasound neuromodulation will be discussed to highlight the key engineering principles, advances in technology and techniques associated with imaging-guided therapy. The goal is to educate engineering students and foster their research interest and development in interdisciplinary and translational biomedical research, applicable to various medical fields. The course is designed for students with diverse backgrounds and research interests, including biophotonics, solid/fluid and computational mechanics, heat transfer, materials science, acoustics, ultrasound instrumentation, and engineering design. Laboratory projects and interaction with clinical doctors will be specifically formulated to enrich the learning experience and encourage students to collaborate in a multidisciplinary team environment to create engineering solutions to address clinical challenges and unmet needs. 3 units.

Instructor: Pei Zhong

Students define a hands-on project of their choice that would prepare them for research in their preferred master’s study concentration. The design process is employed to identify a problem, formulate it, propose design alternatives and rank them, produce and test prototypes, refine and iterate on the selected design. The experiments that are developed are incorporated within the graduate and undergraduate curriculum. The course is composed of equal parts design, technical content, and communication skills (including an online portfolio).

Instructor: George Delagrammatikas

Fundamentals of 3D Printing will provide students with an introduction to 3D printing and will cover both design-for-additive-manufacturing (DFAM) considerations and an overview of the science and technology of four major 3D printing technologies: extrusion-based methods, vat photopolymerization, powder bed fusion, and material jetting. The course will consist of both classroom lectures and hands-on use of various 3D printers in order to learn the basic processes, software, hardware, advantages, and limitations of each method.

Instructor: Ken Gall

Soft matter is a subfield of science that describes the properties and behavior of an important class of materials including polymers, colloids, surfactants, and liquid crystals. The course provides a unified overview of key aspects of the physics of soft condensed matter. The course introduces relevant energies, forces, and time scales governing the interactions of soft materials in bulk and at surfaces. The course will touch upon concepts and phenomena including phase transitions, self-assembly, and viscoelastic behavior of these materials. The main objective of the course is to bring students to a common level of knowledge and competency in soft condensed matter that allows them to pursue more specialized directions in soft matter science.

Instructor: Michael Rubinstein

This course is a broad introduction to medical robotics and surgical technologies. This course covers technical instruction in core areas of surgical technology and requires the completion of three mini-projects where trainees address real-world problems in surgery. Learning objectives include: describe solved and research level problems in robotics, Machine Learning, imaging, and specific surgical applications; debate pros/cons of different surgical robotics applications with respect to current clinical workflow; define a problem and generate a solution that interfaces a specific surgical need and engineering application regarding intellectual property, regulatory and design considerations; and outline a research project based on your prior experience applied to a specific problem in surgery.

Instructors: Siobhan Oca and Brian Mann

This course builds strong programming fundamentals, so that students can use computing to solve problems in your research or other technical pursuits. Students spend half the semester learning programming fundamentals in C and are then introduced to C++ and move on to data structures and algorithms. During the last few weeks of class, you willstudents write a program in C++ that generates a finite element mesh, plans a path for a robot, or performs other functions according to the student’s choice. C and C++ are chosen for the pedagogical benefit of learning to program with “no magic,” for their applications in computer engineering, and because a compiled executable can be very fast. After this course, learning an additional programming language will be much easier. 3 units.

Instructor: Genevieve Lipp

An introductory rheology course for both graduate and senior undergraduate students in MEMS, BME, CEE, Physics and Chemistry. The course will cover the principles of mechanical characterization for soft materials such as polymeric liquids, hydrogels, foodstuffs, soft biological organs/tissues/implants and viscoelastic solids. Prerequisites for this course: basic knowledge of calculus and general physics.

Instructor: Bavand Keshavarz

This course is a challenging introduction to basic concepts used broadly in robotics; it is valuable for students who wish to work in the area. Topics include simulation, kinematics, control, sensing, and system integration. The mathematical basis of each area is emphasized, and concepts are motivated using common robotics applications and programming exercises. You will participate in two projects over the course of the semester, in which you will implement algorithms that apply each of the topics discussed in class to real robotics problems.

Instructor: Siobhan Oca

This course provides an introduction to key concepts in computer simulation, scientific computing and machine learning that are of great relevance across many quantitative disciplines. All the techniques introduced in the course will be motivated with real applications (search engines, heat transport, image and audio recognition, etc.) The first part of the course will cover numerical methods for solving linear systems, nonlinear systems, eigenvalue problems, optimization, signal processing, and transient systems. Building on the first part, we will then introduce fundamentals of machine learning tools for unsupervised and supervised learning. We will cover various algorithms for regression and classification including support vector machines, logistic regression, k-nearest neighbors, and neural networks. In addition, we discuss practical aspects such as data acquisition, feature extraction, training, testing, and performance assessment. The machine learning part will involve a project related to sound/audio recognition.

Instructor: Wilkins Aquino

Introduction to state-space realizations, set theory and optimization. Lyapunov stability theory. Proof of closed-loop stability and persistent feasibility. Computation of quadratic terminal costs and polyhedral constraints for linear systems. Explicit model predictive control and robust receding horizon control will be introduced, time permitting.

Instructor: Leila Bridgeman

Fundamentals and application of statistical mechanics and molecular simulations towards modeling biological and soft materials. Students will learn various computational method including energy minimization, molecular dynamics simulations, Monte Carlo simulations, and stochastic dynamics simulations. Students will obtain valuable hands-on experience in using molecular simulation software, visualizing molecular systems, and analyzing simulation data for computing material properties.

Instructor: Gaurav Arya

The course covers theoretical and numerical aspects of nonlinear optimization. Topics will include optimality conditions for constrained and unconstrained optimization, line search and trust region approaches, Newton and quasi-Newton methods, methods for large scale optimization, and treatment of equality and inequality constraints. The course will have a balance between theory, algorithms and computer implementation.

This course is an in depth research project based experience in medical robotics. Students can look forward to better understanding of their chosen topic, practice applying essential experimental design, working with clinical partners, and presenting their work to stakeholders in medical robotics. Learning objectives include: define a research question in medical robotics that is novel and relevant to their chosen field; identify and relate relevant literature for the methods and topic to their research questions; plan and perform experiments related to their research topic, producing data for subsequent analysis; present research in medical robotics in a poster format to an audience including industry, engineering and clinicians; and work collaboratively on a team to plan and execute a research project.

Instructors: Siobhan Oca and Brian Mann

This is an advanced seminar course on AI-enabled robotics. The key question we will look at is how to build generalist robots that constantly learn, act and improve through natural interactions with the environment. We will study how robots perceive and model the complex world, make plans and decisions, and robustly adapt to various environmental conditions. Students will read, present and discuss the latest research on robot learning which involve areas in robot perception, manipulation, navigation, motion and task planning, robot and sensor design, and multi-robot systems. Throughout this course, students will also conduct a research-level project on robot learning topics.

Instructor: Boyuan Chen

This is a hands-on studio course that expose you how to build a robot from “body” to “brain,” including kinematics, industrial design, manufacturing, electronics, simulation, algorithms, and programming. We aim for a broad overview of robot hardware and software while building a robot from scratch. For this semester, your goal is to design, build, and control an organic-looking legged robot.

Instructor: Boyuan Chen

Characteristics of solar radiation, blackbody model. Wien displacement law, greenhouse effect. Flat plate collectors and evacuated tube collectors, cosine losses, energy balance for flat plate collectors. Thermal storage by sensible heat and latent heat. Fluid mechanics and pumping losses. Trough solar, dish solar, concentration ratio, conversion engines for dish solar. Heliostat systems for power generation, materials for primary and secondary loops, steam cycle. Basics of passive solar architecture.

Instructor: Josiah Knight

This course provides an introduction to the primary concepts associated with space vehicle flight dynamics. Topics discussed, and depending on available time, include orbital mechanics, orbit determination, time of flight, rocket performance, orbital maneuvers, rendezvous, transfers, trajectories, atmospheric entry, and attitude dynamics.

Instructor: Jeffrey Thomas