Cutting-Edge Curriculum
Preparing engineers for the next phase of digital transformation
Digital twin technology is rapidly evolving, and engineering leaders need the most cutting-edge, widely respected approaches to stay ahead. At CMU, you will learn the principles of digital twins as defined by expert researchers in the National Academies of Sciences, Engineering and Medicine. With comprehensive training and hands-on, industry-focused coursework, you will learn how to lead your organization with confidence through the next phase of digital transformation.
Curriculum Overview
When you enroll in the AI Engineering - Digital Twins & Analytics graduate certificate, you will take two graduate-level, credit-bearing courses. Each course will appear on your Carnegie Mellon transcript with the grade earned.
To earn the certificate, you must successfully complete both courses in the program. If you are only interested in one course, however, you may complete that course only and it will show on your transcript with the grade earned.
The certificate includes the following courses taught by CMU faculty:
Principles of Digital Twins
Course Number: 12-830
Number of Units: 12 units
This course will introduce you to the concept of digital twins and digital twin modeling. Not only will you learn how to generate and use digital twin models, but you will also learn how to select an appropriate digital twin environment given specific project requirements.
In addition, you will learn how to build a business case for digital twin adoption, study the role of sensing and information flow within digital twins, and review the role of machine learning in the creation or use of digital twin technology. Finally, you will review the importance of visualization when creating impactful digital twins with different stakeholders and use cases in mind.
By the end of this course, you should be able to:
- Discuss digital twins and digital twin requirements with diverse stakeholders.
- Justify the design of a specific digital twin environment that fulfills project and application requirements.
- Represent and model physical systems within digital environments.
- Understand how information flows between the physical and digital environments.
- Identify challenges and opportunities of integrating digital twins and relevant automated data collection, processing and interpretation techniques in a professional setting.
- Build a case for digital twin adoption.
Digital Twins and AI for Predictive Analytics
Course Number: 12-831
Number of Units: 12 units
This course explores the transformative power of digital twins to harness data-driven insights and improve decision making with predictive analytics. You will study topics like data analysis, statistical inference, and applied machine learning to understand the process of collecting, cleaning, interpreting, transforming, exploring and analyzing data generated by digital twin models.
Using this process, you will learn how to extract pertinent information, communicate insights, and support decision making based on the predictions of how engineered systems might perform under various conditions. The advantages of using visualization techniques to explore data and communicate outcomes will also be highlighted throughout the course.
By the end of this course, you should be able to:
- Plan, design, and implement projects using statistical, computational, and quantitative applied machine learning techniques
- Predict system response to support data-driven decision making using digital twins
- Discuss the ethical implications of AI-driven decision making
Meet Our World-Class Faculty
Dr. Pingbo Tang
Associate Professor of Civil and Environmental Engineering
Education: Ph.D., Carnegie Mellon University
Research Focus: Remote sensing, human systems engineering, and information modeling technology in support of the spatiotemporal analyses needed to effectively manage workspaces, constructed facilities, and civil infrastructure systems. Examining sensing and modeling methods for understanding the Human-Cyber-Physical-Systems (H-CPS) in accelerated construction and infrastructure operations (e.g., airport and power plant operations, water treatment plant control).
Affiliated Groups & Centers
- Research Lab: Spatiotemporal Workflows and Resilient Management (SWARM) Laboratory
- Research Groups: Intelligent Engineered Systems and Society (IESS); Sustainable Energy and Transportation Systems (SETS); Climate-Resilient Environmental Systems and Technologies (CREST)
Dr. Mario Bergés
Professor of Civil and Environmental Engineering
Education: Ph.D., Carnegie Mellon University
Research Focus: Making our built environment more operationally efficient and robust through the use of information and communication technologies, so that it can better deal with future resource constraints and a changing environment. Bergés’ current work focuses on developing approximate inference techniques to extract useful information from sensor data coming from civil infrastructure systems, with a particular focus on buildings and energy efficiency.
Affiliated Groups & Centers
- Research Center and Research Lab: Smart Infrastructure Institute (SII) and Intelligent Infrastructure Research Lab (INFER Lab)
- Research Groups: Intelligent Engineered Systems and Society (IESS); Sustainable Energy and Transportation Systems (SETS); Climate-Resilient Environmental Systems and Technologies (CREST)
The Graduate Certificate in AI Engineering - Digital Twins & Analytics is offered by the Department of Civil & Environmental Engineering (CEE), which is housed within CMU’s highly-ranked College of Engineering. CEE faculty members are highly distinguished in their field and many of them are currently collaborating on high-profile projects with digital twin technology. Check out some of their work below:
Intelligent maintenance for military aviation assets (funded by the U.S. Army Research Laboratory)
Environmental control and life support systems (ECLSS) for deep space missions (funded by NASA)
Sustainable and equitable food delivery models
AI for the national aviation system (part of a NASA University Led Initiative)
The Building Blocks of Our Curriculum
Comprehensive Training for AI Implementation
CMU’s online graduate certificate provides comprehensive training for each phase of the AI and digital twin implementation process. From pre-implementation planning, to identifying components of a digital twin, to understanding the bidirectional relationship between a physical and virtual environment, to the ethical decision-making that needs to be considered during implementation, you will gain the skills needed to lead the successful integration of AI and digital twins into decision workflows of your organization.
A Human-Centered Approach
Learning when and how to implement a digital twin is critical to the successful implementation of this technology, but understanding how humans interact with digital twins is equally important. Digital twins are designed to support humans, not replace them, says faculty member Dr. Pingbo Tang. In this program, you will learn the components of a digital twin, but you will also learn how humans use digital twins to make ethical decisions on a large scale. This knowledge will help you implement the technology more effectively for your organization.
Industry-Focused Coursework
The coursework in this certificate moves beyond theory to provide you with knowledge that you can immediately apply in the workplace. Through hands-on experiences, you’ll learn how digital twins are used to diagnose problems in water treatment plants, allocate resources for construction projects, control the air quality of buildings, and even maintain renewable energy sources. By featuring real-world examples from both traditional and nontraditional fields of civil and environmental engineering, this certificate equips you to make an impact right from the start.