Digital Twin Applications in Automotive Engineering
Digital Twin Applications in Automotive Engineering
Published 8/2025
Duration: 2h 1m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 597.26 MB
Genre: eLearning | Language: English
Published 8/2025
Duration: 2h 1m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 597.26 MB
Genre: eLearning | Language: English
A Complete Guide to Digital Twins in Automotive: Learn Vehicle Simulation, Virtual Testing & Real-Time Diagnostics
What you'll learn
- Mechanical, Mechatronics, and Automotive Engineers
- Embedded Systems and Control Engineers
- Product Development and R&D Teams
- Systems Engineers & MBSE Practitioners
- Fleet Operations Managers & Maintenance Engineers
- Graduate Students & Researchers in Automotive or Systems Engineering
- Professionals transitioning into Automotive AI, IoT, or Digital Manufacturing
Requirements
- Basic knowledge of mechanical or automotive systems Familiarity with engineering design or simulation concepts No prior experience with digital twins or programming needed Optional: Exposure to tools like MATLAB, CAD, or Python is helpful but not required
Description
The future of automotive engineering is here — and it’s digital, dynamic, and data-driven.
In this in-depth course, you'll explore howDigital Twin technologyis transforming the design, testing, optimization, and lifecycle management of modern vehicles. From high-fidelity simulation to predictive maintenance and real-time telemetry, digital twins are changing how we build and operate intelligent, high-performance automotive systems.
Whether you're an engineer, product developer, systems architect, or technical leader — this course will equip you with the tools and frameworks tounderstand, apply, and leadin the age of digital twins.
What You’ll Learn
Core concepts behind digital twins and their evolution in automotive systemsHow digital twins are applied across design, simulation, control, and testingThe role of MBSE (Model-Based Systems Engineering) and digital thread integrationFleet-wide data collection, edge computing, and cloud-twin synchronizationPredictive maintenance, OTA updates, and real-world industry case studiesFuture trends: AI-augmented twins, quantum simulation, neuromorphic control, and blockchain
Who This Course is For
Mechanical, Automotive, and Mechatronics Engineers
Embedded Systems and Control Engineers
R&D and Product Development Teams
MBSE Practitioners and Systems Engineers
Fleet Managers, Diagnostics, and Reliability Professionals
Graduate Students and Industry Transitioners exploring AI, IoT, or Smart Manufacturing
What You Need
A basic understanding of mechanical or automotive systems
Familiarity with engineering modeling, simulation, or system design
No prior experience with digital twins, AI, or programming is required
By the end of this course, you’ll be able todesign and deploy digital twin frameworks, connect physical systems to simulation environments, and understand how data, physics, and software combine to drivereal-time decisions across the automotive product lifecycle.
Join us and build the future of automotive innovation, one twin at a time.
Who this course is for:
- Basic understanding of mechanical or automotive systems Familiarity with engineering concepts like simulation or control systems No prior experience with digital twin tools or AI required Optional: experience with CAD, MATLAB, or Python is helpful but not necessary This course is designed to guide you step-by-step — from foundational concepts to real-world applications.
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