Mentor Based
Digital Twins: Predictive Modeling for Dynamic Industrial Processes
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92% Booked
07
Hrs
:
34
Min
:
34
Sec
Program Syllabus
Module 1
About This Course
This workshop focuses on using Digital Twins for predictive modeling in dynamic industrial processes. Participants will learn how to create virtual models of physical assets, optimize operations, and predict system behaviors in real-time. Through a mix of theory and hands-on sessions, attendees will explore how digital twins, combined with AI and machine learning, can enhance decision-making, reduce risks, and improve efficiency in industrial environments.
Module 2
Aim
The aim is to teach participants how to use Digital Twins and predictive modeling to optimize industrial processes, improve efficiency, and enhance decision-making using real-time data and AI.
Module 3
Workshop Objectives
- Understand Digital Twin Technology: Learn the core concepts and applications of digital twins in industrial processes.
- Predictive Modeling: Explore how predictive modeling can be used to forecast system behaviors and optimize operations.
- Real-Time Data Integration: Gain hands-on experience in integrating real-time data into digital twins for continuous monitoring and decision-making.
- AI and Machine Learning Integration: Understand how AI and machine learning enhance the predictive capabilities of digital twins.
- Industry Applications: Discover practical applications of digital twins across various industries, including manufacturing, energy, and logistics.
- Hands-On Experience: Engage in practical sessions using tools and software to implement digital twin solutions in industrial contexts.
Module 4
Day 1: Advanced Time-Series Forecasting with LSTMs
- Master LSTM networks for dynamic event forecasting (e.g., crystal diameter)
- Handle temporal dependencies in sensor data
- Hands-on: Build a forecasting model using sensor data
Module 5
Day 2: Sensor Fusion and Multimodal Machine Learning
- Fuse multisource data (sensor and static parameters) for better predictions
- Enhance accuracy with multimodal learning
- Hands-on: Build a Multi-Input Neural Network for data fusion
Module 6
Day 3: Anomaly Detection for Industrial Safety
- Detect early-stage failures with Autoencoders
- Build anomaly detection models for safety applications
- Hands-on: Train and test an anomaly detection model
Module 7
Day 4: Interpretable AI Models for Reliability
- Use SHAP and LIME for model interpretability
- Visualize feature importance for transparent AI decisions
- Hands-on: Generate SHAP/LIME plots for feature analysis
Module 8
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Module 9
Important Dates
Module 10
Registration Ends
01/31/2026
IST 04:30 PM
Module 11
Workshop Dates
01/31/2026 – 02/03/2026
IST 5: 30PM
Module 12
Workshop Outcomes
- Proficiency in Digital Twin Technology: Participants will gain a solid understanding of digital twins and their applications in dynamic industrial processes.
- Skill in Predictive Modeling: Attendees will be able to apply predictive modeling techniques to optimize industrial systems and processes.
- Hands-On Experience: Participants will gain practical experience in integrating real-time data and using AI and machine learning in digital twins.
- Improved Decision-Making: Participants will be equipped with the tools to make data-driven decisions, reducing risks and improving operational efficiency.
- Industry-Relevant Knowledge: Attendees will be able to apply digital twin technology in their own industries, enhancing performance and innovation.
Module 13
Fee Structure
Module 14
Student
₹1999 | $60
Module 15
Ph.D. Scholar / Researcher
₹2999 | $70
Module 16
Academician / Faculty
₹3999 | $80
Module 17
Industry Professional
₹5999 | $100
Module 18
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Module 19
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 20
Publication Opportunity
Get published in a prestigious open-access journal.
Module 21
Centre of Excellence
Become part of an elite research community.
Module 22
Networking & Learning
Connect with global researchers and mentors.
Module 23
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 24
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