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Home >Courses >Digital Twins: Predictive Modeling for Dynamic Industrial Processes

01/31/2026

Registration closes 01/31/2026
Mentor Based

Digital Twins: Predictive Modeling for Dynamic Industrial Processes

Unlock the Future of Industry: Harness the Power of Predictive Modeling with Digital Twins

  • Mode: Virtual / Online
  • Type: Mentor Based
  • Level: Moderate
  • Duration: 3 Days
  • Starts: 31 January 2026
  • Time: 5: 30PM IST

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.

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.

Workshop Structure

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

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.

Important Dates

Registration Ends

01/31/2026
IST 04:30 PM

Workshop Dates

01/31/2026 – 02/03/2026
IST 5: 30PM

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.

Fee Structure

Student

₹1999 | $60

Ph.D. Scholar / Researcher

₹2999 | $70

Academician / Faculty

₹3999 | $80

Industry Professional

₹5999 | $100

What You’ll Gain

  • Live & recorded sessions
  • e-Certificate upon completion
  • Post-workshop query support
  • Hands-on learning experience

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