Syllabus1
Digital Twins for Physical Systems - CSE 8803 - DTP
Course Learning Objectives
By the end of the semester, you will be made familiar with …
- the concept of Digital Twins and how they interact with their environment
- Statistical Inverse Problems and Bayesian Inference
- techniques from Data Assimilation (DAT), Simulation-Based Inference (SBI), and Recursive Bayesian Inference (RBI), and Uncertainty Quantification [UQ]
For more on the Course outline, Topics, and Learning goals, see Goals.
For a motivational article see Digital Twins in the era of generative AI and this Podcast
Course Material
Textbooks
| Data assimilation: methods, algorithms, and applications | Asch, Bocquet, and Nodet (2016), Mark | SIAM, 2016 |
| A toolbox for digital twins: from model-based to data-driven | Asch (2022), Mark | SIAM, 2022 |
| Notebook: Kalman and Bayesian Filters in Python | Roger R. Labbe | Private, 2020 |
| Understanding Deep Learning | Prince (2023) | MIT, 2024 |
- These electronic books are only available when online at Georgia Tech!
- The Python notebook is hands on, very practical, and contains very useful exercises with answers. You are encouraged to explore and we will incorporate material from this notebook in this course.
Journal Papers
| A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies | Thelen et al. (2022) | Springer, 2022 |
| A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives | Thelen et al. (2023) | Springer, 2022 |
| Sequential Bayesian inference for uncertain nonlinear dynamic systems: A tutorial | Tatsis, Dertimanis, and Chatzi (2022) | Arxiv, 2022 |
Additional reading material
Details on readings will be provided on a lecture by lecture basis following this Schedule.
Lectures & computational labs
The lectures and computational assignments will be posted under Schedule.
In part the course is an adaptation of open-source Basic and Advanced courses (CSU-IMU-2023) developed by Mark Asch. Codes will be drawn from the course and from the book. During the second half of the course, we will draw from the current literature.
Course community
Inclusive community
It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength, and benefit. It is my intent to present materials and activities that are respectful of diversity. Your suggestions are encouraged and appreciated.
Discrimination, Harassment, and Sexual Misconduct
To maintain a safe learning environment that fosters the dignity, respect, and success of students, faculty, and staff, Tech prohibits discriminatory harassment, which is unwelcome verbal, nonverbal, or physical conduct directed at any person or group based upon race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or veteran status that has the purpose or effect of creating an objectively hostile working or academic environment.
Accessibility
If there is any portion of the course that is not accessible to you due to challenges with technology or the course format, please let me know so we can make appropriate accommodations.
Disability Services are available to ensure that students are able to engage with their courses and related assignments. Students should be in touch with the Student Disability Access Office to request or update accommodations under these circumstances.
Communication
All lecture notes, assignment instructions, an up-to-date schedule, and other course materials may be found on the course website.
Announcements will be shared on Piazza.
Activities & Assessment
Computational Assignments
Details on the assignments will be provided on a lecture by lecture basis following this Schedule. These assignments will mostly involve Digital Twin-related programs, computer simulations, and data analysis. These assignments are designed for each student to work one by him/herself. Collaboration is to a reasonable degree encouraged. During these assignments, you will apply concepts discussed during lectures, with a focus on the computational aspects. These assignments will count towards 30% of your overall course grade. There will be 8-10 computational assignments during the term.
Exams
There will be no exams.
Final Project
The purpose of the Final Project is to apply what you’ve learned throughout the semester to analyze interesting and relevant research questions related to Digital Twins. See Final Project for details on requirements and grading.
Grading
The final course grade will be calculated as follows:
| Category | Percentage |
|---|---|
| Comp. Lab assignments | 30% |
| Presentation Journal Paper | 20% |
| Final Project & Presentation | 50% |
Important dates
Jan 5: Phase II Registration
Jan 12: Classes begin
Jan 16: Deadline for Registration/Schedule Changes closes
March 18: Last day to withdraw
March 18 Presentation of 5 min pitch
March 20 Submission Project Proposal
March 23-27: Spring break
March 30 - onwards In-class Seminar presentations
April 27: Final Instructional Class
April 27 and 29 Final Project Presentations
May 1 Submission Final Project Report
Course policies
Academic honesty
Please abide by the following as you work on assignments in this course:
You may discuss individual lab assignments with other students; however, you may not directly share (or copy) code or write up with other students. For team assignments, you may collaborate freely within your team. You may discuss the assignment with other teams; however, you may not directly share (or copy) code or write up with another team. Unauthorized sharing (or copying) of the code or write up will be considered a violation for all students involved.
Reusing code: Unless explicitly stated otherwise, you may make use of online resources (e.g. StackOverflow) for coding examples on assignments. If you directly use code from an outside source (or use it as inspiration), you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.
Using AI-powered coding tools and agents: Use of tools such as Claude Code, Codex, ChatGPT, Gemini, and GitHub Copilot is permitted, but you must abide by the following principles:
- Disclosure (required): If you used a generative AI tool for any part of an assignment or project, include an AI Usage Statement in your report/README that names the tool(s) and briefly describes what you used them for (e.g., debugging, drafting, test generation, refactoring).
- Provenance (required): Maintain a prompt/interaction log (e.g.,
PROMPTS.mdor aprompts/folder) and commit it to your GitHub repository. Commits should reflect meaningful iterations and should reference prompt IDs or excerpts so the evolution of your work can be audited. - Accountability (required): You are responsible for everything you submit. You must be able to explain your code and results, reproduce your experiments, and verify any factual claims or citations. You may be asked to walk through your work (code, derivations, and/or experiments).
- Spec-driven workflow: For the final project, you must use a specification-driven workflow (e.g., spec-kit, agent-os, or cc-sdd, or an equivalent workflow that includes a written spec and acceptance tests). For labs, this is strongly encouraged.
- Privacy, IP, and licensing: Do not paste confidential, proprietary, or sensitive data into external AI services. Ensure you comply with software licensing and provide attribution where appropriate.
General guidance on the use of Generative AI
AI-based assistance (e.g., ChatGPT, Claude, Gemini, Copilot, and coding agents) can be a useful study and software-development aid in this course. You may use these tools to support learning the lecture/textbook material and to help develop your ideas for proposals, labs, and the final project. However, these tools do not replace your responsibility for independent work, correctness, and academic integrity.
Required documentation when you use Generative AI (applies to labs and the final project)
If you used generative AI for any part of your work, your submission must include:
- AI Usage Statement (in your report/README): tool name(s) + what you used them for + what you independently verified.
- Prompt/interaction log (in your GitHub repo): e.g.,
PROMPTS.mdor aprompts/folder (redact anything sensitive). - Verification evidence (in your repo): tests, sanity checks, references to derivations, or short notes describing how you validated AI-assisted outputs.
Example AI Usage Statement (you may copy/adapt):
Disclosure: I used generative AI tools (tool names and versions, if known) to support portions of this assignment (briefly describe: e.g., brainstorming, debugging, refactoring, drafting). I curated prompts, reviewed and edited all outputs, verified factual statements and citations, and take full responsibility for the final submission. A prompt/provenance record is included in the repository.
Generating research ideas or approaches
- Brainstorming: You can use an AI tool as a brainstorming partner. Treat it as an iterative process—try multiple prompts, request alternatives, and compare suggestions.
- Surveying existing approaches: AI tools can provide an initial overview of methods or literature, but you must verify claims and consult authoritative sources (papers, textbooks, and library/professional-society databases).
- Prompting skill matters: The value of the output depends on the quality of the prompt. Keep prompts specific, include constraints, and document the versions you tried.
Allowed uses (examples)
- Explaining error messages, suggesting debugging strategies, or proposing minimal reproducible examples.
- Generating unit tests, docstrings, documentation, or refactoring suggestions that you then review and validate.
- Brainstorming model/design choices, experiment plans, ablations, and evaluation criteria.
- Drafting pseudocode or outlines that you then implement and verify yourself.
Not allowed uses (examples)
- Submitting AI-generated code, text, figures, results, or citations that you cannot explain or reproduce.
- Using AI to fabricate citations, data, experiments, or results (all citations must be verifiable).
- Using AI to generate a complete solution and only lightly paraphrasing/editing it as your own work.
- Sharing private course materials or other students’ work with an external AI tool, or pasting others’ code/text into a prompt.
Advice on usage
- Be critical: Do not trust any output you cannot independently evaluate or trace back to credible sources.
- Be scientific with prompts: Prompting is not deterministic; small changes can yield different answers. Keep records and compare outputs across prompt variants.
- Protect privacy and IP: Do not share confidential, proprietary, or sensitive information (including personally identifiable information) with external AI services. Treat prompts and uploads as potentially non-private. If you intend to pursue commercialization or other Intellectual Property avenues, uploading information to an open AI platform may constitute disclosure.
Late work & extensions
The due dates for assignments are there to help you keep up with the course material and to ensure the teaching team can provide feedback within a timely manner. We understand that things come up periodically that could make it difficult to submit an assignment by the deadline. Note that the lowest lab assignments will be dropped to accommodate such circumstances.
Campus Resources for Students
Links to Campus Resources for Students — e.g. Center for Academic Success, Communication Center, Office of Disability Services, OMED, Division of Student Life, etc. available at: https://bpb-us-e1.wpmucdn.com/sites.gatech.edu/dist/d/4626/files/2025/07/campus_resources_students.pdf
Quick Guide on Student Mental Health and Wellness Resources:
The Center for Mental Health Care & Resources is here to offer confidential support and services to students in need of mental health care. During regular business hours, students who are not actively in counseling may call 404-894-2575 or walk-in to the office , 353 Ferst DR NW Atlanta GA 30313 (Flag building next to the Student Center). Any time outside of business hours, students may call 404-894-2575 and select the option to speak to the after-hours counselor.
Additional On Campus Options
Students who are experiencing an immediate life-threatening emergency on campus, can call the Georgia Tech Campus Police at 404-894-2500
Local and National crisis resources available 24/7:
- Crisis Text Line: Text HOME to 741741 https://www.crisistextline.org/
- Georgia Crisis & Access Line: Call 1-800-715-4225
- National Suicide Prevention Lifeline: Call 988 or 1-800-273-8255, or access text chat here https://suicidepreventionlifeline.org/chat/
Additional services offered by Satellite counselors’ (walk-in consultation hours):
Each Satellite counselor provides weekly consultation appointments at varying times based on their schedules (see highlighted section below for my Fall ’23 hours). Anyone can access a Satellite counselor during these times, to set up a consultation email is preferable, once a message is received the Satellite counselor will reply with available dates/times and a link. Some Satellite counselors will accommodate in-person walk-ins at these times as well.
What it is
- Consulting about a brief, non-emergency concern during the counselor’s posted walk-in consultation hours (similar to Let’s Talk). Consultations are approximately 15 minutes.
- Providing information to students about mental health resources on campus and how to get connected
- Consulting with faculty/staff about a student of concern
What it isn’t
- Not for a walk-in counseling appointment or initial assessment
- Not for students who are seeking support in their counselor’s absence. Students who need additional/urgent support when their counselor is unavailable should visit the Center for Mental Health Care & Resources
- Not for crisis
- Not for case management
Tara Holdampf, MS, LPC, (she/her), Satellite Counselor’s Consultation Hours for the College of Sciences: Please email tara.holdampf@studentlife.gatech.edu for an appointment, or stop by my Office Location: MoSE 1120B. Monday-Friday 3:00 PM - 4:00 PM
Other useful web pages to explore:
Wellness Empowerment Center
Student Engagement and Wellbeing
Health, Wellness & Recreation
Tech Ends Suicide Mental Health Services
References
Footnotes
Syllabus updates: This syllabus is subject to change throughout the semester. We will try to keep these updates to a minimum. ↩︎