Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
PINNs for Battery & Material Science teaches a hybrid modelling approach for materials. You learn how physics-informed neural networks embed governing physical equations — diffusion, reaction, electrochemistry — into machine learning, and why that produces reliable models of battery and materials behaviour where data alone falls short. The course connects PINNs to real materials-modelling problems. You finish able to reason about applying physics-informed neural networks to a materials or battery problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies physics-informed neural networks (PINNs) to battery and materials science — embedding physical laws into ML to model materials behaviour reliably.
📋 Course Objectives
1. Explain physics-informed neural networks.
2. Embed governing equations into models.
3. Model diffusion and electrochemical behaviour.
4. Apply PINNs to battery and materials problems.
5. Combine physics with sparse data.
👥 Who Should Enroll?
• Materials and battery researchers
• Scientific-ML practitioners
• Computational-materials scientists
• Students of scientific computing
🚀 Key Learning Outcomes
• An understanding of PINNs for materials.
• A physics-plus-ML perspective.
• A materials-modelling project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Pinns For Battery & Material Science Foundations
Implement Battery with Material for practical ai fundamentals, mathematics, and pinns for battery & material science foundations applications and outcomes. • Design PINNs with sustainability for practical ai fundamentals, mathematics, and pinns for battery & material science foundations applications and outcomes. • Analyze Battery with Material for practical ai fundamentals, mathematics, and pinns for battery & material science foundations applications and outcomes.
Data Engineering, Preprocessing, and Feature Pipelines
Implement Battery with Material for practical data engineering, preprocessing, and feature pipelines applications and outcomes. • Design PINNs with sustainability for practical data engineering, preprocessing, and feature pipelines applications and outcomes. • Analyze Battery with Material for practical data engineering, preprocessing, and feature pipelines applications and outcomes.
Model Architecture, Algorithm Design, and Pinns For Battery & Material Science Methods
Implement Battery with Material for practical model architecture, algorithm design, and pinns for battery & material science methods applications and outcomes. • Design PINNs with sustainability for practical model architecture, algorithm design, and pinns for battery & material science methods applications and outcomes. • Analyze Battery with Material for practical model architecture, algorithm design, and pinns for battery & material science methods applications and outcomes.
Training, Hyperparameter Optimization, and Evaluation
Implement Battery with Material for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects. • Design PINNs with sustainability for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze Battery with Material for practical training, hyperparameter optimization, and evaluation applications and outcomes. Gain hands-on experience and produce real-world projects.
Deployment, MLOps, and Production Workflows
Implement Battery with Material for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Design PINNs with sustainability for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects. • Analyze Battery with Material for practical deployment, mlops, and production workflows applications and outcomes. Gain hands-on experience and produce real-world projects.
Ethics, Bias Mitigation, and Responsible AI Practices
Implement Battery with Material for practical ethics, bias mitigation, and responsible ai practices applications and outcomes. • Design PINNs with sustainability for practical ethics, bias mitigation, and responsible ai practices applications and outcomes. • Analyze Battery with Material for practical ethics, bias mitigation, and responsible ai practices applications and outcomes.
Industry Integration, Business Applications, and Case Studies
Implement Battery with Material for practical industry integration, business applications, and case studies applications and outcomes. • Design PINNs with sustainability for practical industry integration, business applications, and case studies applications and outcomes. • Analyze Battery with Material for practical industry integration, business applications, and case studies applications and outcomes.
Advanced Research, Emerging Trends, and Pinns For Battery & Material Science Innovations
Implement Battery with Material for practical advanced research, emerging trends, and pinns for battery & material science innovations applications and outcomes. • Design PINNs with sustainability for practical advanced research, emerging trends, and pinns for battery & material science innovations applications and outcomes. • Analyze Battery with Material for practical advanced research, emerging trends, and pinns for battery & material science innovations applications and outcomes.
Capstone: End-to-End Pinns For Battery & Material Science AI Solution
Implement Battery with Material for practical capstone: end-to-end pinns for battery & material science ai solution applications and outcomes. • Design PINNs with sustainability for practical capstone: end-to-end pinns for battery & material science ai solution applications and outcomes. • Analyze Battery with Material for practical capstone: end-to-end pinns for battery & material science ai solution applications and outcomes.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Battery |
| Covered Tool / Platform | Material |
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