AI-Enabled Digital Twins For Thermal/Fluids & System Performance
This online engineering CPD course examines AI-enhanced digital twins for thermal-fluid systems and overall system performance.
Digital twins are dynamic digital representations of physical systems that combine engineering models and operational data to analyze performance, evaluate scenarios, and support engineering decision-making.
Thermal-fluid systems involve complex interactions among operating conditions, physical behavior, equipment performance, and system controls, requiring accurate modeling, uncertainty assessment, and engineering oversight to maintain safety and reliability.
This course covers hybrid digital twins that integrate physics-based simulations, including CFD, FEA, and thermal network models, with AI/ML surrogate models for rapid what-if analysis and control optimization. It also emphasizes model Verification & Validation (V&V), uncertainty quantification, calibration, configuration control, risk-based decision-making, design optimization, performance monitoring, control tuning, and detection of model drift.
This 1 CPD online course is applicable to mechanical and chemical engineers, as well as other technical professionals who are interested in learning more about AI-enhanced digital twins for thermal-fluid systems and system performance.
This PE continuing education course is intended to provide you with the following specific knowledge and skills:
- Understanding hybrid digital twins that combine physics-based thermal-fluid models with AI/ML surrogate models
- Familiarizing with the benefits, limitations, and speed-versus-fidelity trade-offs of physics-based simulations and ML surrogates
- Learning how to develop and integrate ML surrogate models for rapid scenario evaluation and control optimization
- Understanding model calibration, Verification & Validation (V&V), and uncertainty quantification for reliable digital twins
- Learning how digital twins support design optimization, real-time performance monitoring, and predictive control
- Familiarizing with model drift, configuration control, traceability, and risk-based frameworks such as FMEA
- Understanding how to communicate digital twin results with transparent assumptions, safety margins, and professional engineering judgment
In this professional engineering CEU course, you need to review the course document titled, “AI-Enabled Digital Twins For Thermal/Fluids & System Performance”, prepared by Brian Lisiewski, P.E.
Once you complete your course review, you need to take a multiple-choice quiz consisting of ten (10) questions to earn 1 CPD credits. The quiz will be based on the entire document.
Upon successful completion of the quiz, print your Certificate of Completion instantly. (Note: if you are paying by check or money order, you will be able to print it after we receive your payment.) For your convenience, we will also email it to you. Please note that you can log in to your account at any time to access and print your Certificate of Completion.