International Workshop on Accelerating Material Innovation through Artificial Intelligence
“AI-Assisted Composite Materials Design” is an international, hands-on workshop that explores how AI is transforming traditional materials science workflows. Participants will learn to use data-driven models, surrogate optimization, and deep learning algorithms to predict material properties, simulate behavior, and discover new composite formulations with tailored mechanical, thermal, or electrical properties.
The workshop emphasizes real-world datasets, multi-scale modeling, and AI-powered tools like Bayesian optimization, Neural Networks, Graph Neural Networks (GNNs), and AutoML platforms applied to composite design and simulation.
To equip participants with practical knowledge and tools to leverage Artificial Intelligence and Machine Learning for the design, modeling, and optimization of composite materials, enabling accelerated innovation in aerospace, automotive, energy, and biomedical applications.
Bridge the gap between materials science and artificial intelligence
Train participants to use AI for faster, cost-effective materials discovery
Foster cross-disciplinary collaboration for smart, sustainable material development
Introduce scalable digital tools for next-generation composite design
Promote reproducibility, transparency, and innovation in AI-assisted materials research
Module 1: Generative Models for Microstructure Design
Fundamentals of microstructure design and its impact on material properties
Traditional vs. data-driven design approaches
Overview of generative models: GANs, VAEs, and diffusion models
Conditioning generative models on target properties
Learning inverse design: mapping structure to desired properties
Case studies in 2D/3D material generation using machine learning
Module 2: Bayesian Optimization for Stiffness/Weight Trade-Off
Multi-objective design problems in engineering
Stiffness vs. weight trade-offs in materials and components
Constraints in mechanical and aerospace design
Principles of Bayesian optimization: Gaussian processes, surrogate models, acquisition functions
Pareto frontiers and uncertainty quantification
Module 3: Digital Twin Validation in Finite Element Analysis (FEA)
Introduction to digital twins in predictive engineering
Integrating simulation data with real-world observations
FEA model setup and validation for structural behavior
Techniques for model calibration using experimental or sensor data
AI-assisted model updates to enhance simulation fidelity and performance feedback
Materials and mechanical engineers
Polymer scientists and nanocomposite researchers
AI/ML engineers in manufacturing or R&D
Aerospace, automotive, and biomedical materials developers
UG/PG/PhD students in materials science, physics, or applied AI
Understand AI workflows for composite material property prediction
Learn how to build and deploy surrogate models for material optimization
Integrate structure-property relationships into predictive ML pipelines
Evaluate model performance for multi-objective materials design
Receive international certification and gain reusable tools for research and industry
₹2,499
$50
The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.
365 days from your registration, on your NanoSchool dashboard.
Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
Live, mentor-led and certified. Dates and times in IST.
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