Digital Twins: Predictive Modeling for Dynamic Industrial Processes
Build Intelligent Industrial Digital Twins Using Python, Simulate Dynamic Processes, Predict System Behavior & Optimize Industrial Performance with AI
Skills you will gain:
About Workshop:
Digital twins are transforming industrial research and engineering by connecting physical processes with computational models, sensor data and predictive analytics. These technologies enable researchers and engineers to simulate dynamic system behavior, monitor operating conditions, identify potential faults and evaluate process improvements. Their applications are expanding across smart manufacturing, chemical processing, energy systems, industrial automation and predictive maintenance.
This advanced three-day hands-on workshop introduces researchers to the development of AI-driven digital twins for dynamic industrial processes. Participants will explore physics-based process modeling, industrial sensor data analysis, machine learning-based prediction, anomaly detection and simulation-driven optimization. Using Python and Google Colab, participants will develop a simulated industrial digital twin, integrate process measurements, predict operating behavior and evaluate different process-control scenarios.
The workshop emphasizes practical computational skills, hybrid physics–AI modeling and reproducible workflows applicable to industrial research and intelligent manufacturing.
Aim:
To equip researchers, academicians and industry professionals with practical skills in developing AI-driven industrial digital twins by integrating dynamic process simulation, machine learning, predictive analytics and optimization techniques for intelligent industrial systems.
Workshop Objectives:
- Understand digital twin architectures, cyber–physical systems and their applications in industrial process engineering.
- Develop mathematical and physics-based models to simulate dynamic industrial processes.
- Analyze industrial sensor data and integrate process measurements with digital models.
- Apply machine learning techniques to predict process behavior and identify abnormal operating conditions.
- Explore hybrid physics–AI modeling approaches for improving process prediction.
- Perform what-if simulations and optimize process parameters using computational techniques.
- Evaluate model performance, prediction reliability and practical limitations of industrial digital twins.
What you will learn?
Day 1: Industrial Digital Twin Architecture, Dynamic Process Modeling & Simulation
- Understand digital twin fundamentals, cyber–physical systems and Industry 4.0/5.0 architectures for smart manufacturing.
- Explore industrial process dynamics, mass and energy balances, and physics-based modeling using Python and SciPy.
- Integrate sensor data, state synchronization and model calibration for dynamic process simulation.
- Hands-on: Build a tank-level digital twin, integrate simulated sensor data and visualize process behavior using Google Colab, Python, SciPy, NumPy and Matplotlib.
Day 2: AI-Based Process Prediction, Hybrid Modeling & Anomaly Detection
- Analyze industrial time-series data, process variability and time-lag features for predictive modeling.
- Develop machine learning models using Random Forest and gradient boosting for dynamic process forecasting.
- Apply hybrid physics–AI modeling, residual learning and Isolation Forest for predictive digital twins and anomaly detection.
- Evaluate prediction accuracy using MAE, RMSE and time-ordered validation.
- Hands-on: Train a hybrid predictive digital twin using simulated industrial sensor data, forecast process behavior and detect anomalies using Google Colab, pandas, scikit-learn and SciPy.
Day 3: Digital Twin Optimization, What-If Simulation & Intelligent Decision-Making
- Explore process optimization, operating constraints and objective functions for intelligent industrial operations.
- Perform what-if simulations to evaluate flow rates, disturbances and operating setpoints.
- Understand model predictive control (MPC), simulation-based optimization and do-mpc for decision support.
- Assess model drift, prediction reliability and uncertainty for industrial digital twin validation.
- Hands-on: Simulate operating scenarios, optimize process-control parameters and compare performance using Google Colab, SciPy Optimize, NumPy and Matplotlib.
Mentor Profile
Fee Plan
Important Dates
14 Nov 2026 Indian Standard Timing 04:30 PM
12 Nov 2026 to 14 Nov 2026 Indian Standard Timing 5: 30PM
Get an e-Certificate of Participation!
Intended For :
- Researchers and Research Scientists: Working in digital twins, industrial process modeling, predictive analytics, smart manufacturing and cyber–physical systems.
- PhD Scholars and Postdoctoral Researchers: Investigating dynamic system modeling, industrial AI, process simulation, intelligent control and optimization.
- Academicians and Faculty Members: Teaching or conducting research in mechanical, chemical, electrical, industrial and manufacturing engineering.
- Industrial Engineers and R&D Professionals: Involved in process improvement, industrial automation, operational efficiency and data-driven engineering.
- Automation and Control Engineers: Interested in predictive process modeling, industrial sensor analytics and simulation-based decision support.
- Data Scientists and AI Professionals: Seeking to apply machine learning and predictive analytics to industrial processes and engineering systems.
- Postgraduate Students: From engineering, applied sciences, industrial automation and related disciplines interested in computational modeling and intelligent manufacturing.
Recommended Background: Basic knowledge of engineering systems, mathematical modeling or industrial processes is helpful. Familiarity with Python or machine learning is beneficial but not mandatory for participating in the guided hands-on exercises.
Career Supporting Skills
Workshop Outcomes
- Develop and simulate dynamic industrial process models using Python and SciPy.
- Process and visualize industrial sensor data for digital twin applications.
- Build predictive machine learning models using scikit-learn.
- Detect process anomalies using statistical and machine learning techniques.
- Integrate physics-based simulations with data-driven prediction methods.
- Perform scenario-based simulations and optimize operating parameters.
- Generate research-quality visualizations and interpret results for industrial decision-making.