Workshop Registration End Date :31 Jan 2026
Virtual Workshop
Digital Twins: Predictive Modeling for Dynamic Industrial Processes
Unlock the Future of Industry: Harness the Power of Predictive Modeling with Digital Twins
Skills you will gain:
About Workshop:
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.
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.
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.
What you will learn?
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
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
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
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
Mentor Profile
Fee Plan
StudentINR 1999/- OR USD 60
Ph.D. Scholar / ResearcherINR 2999/- OR USD 70
Academician / FacultyINR 3999/- OR USD 80
Industry ProfessionalINR 5999/- OR USD 100
Important Dates
Registration Ends
31 Jan 2026 Indian Standard Timing 04:30 PM
31 Jan 2026 Indian Standard Timing 04:30 PM
Workshop Dates
31 Jan 2026 to 03 Feb 2026 Indian Standard Timing 5: 30PM
31 Jan 2026 to 03 Feb 2026 Indian Standard Timing 5: 30PM
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
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.