Programme Parameters
Graduate / Intermediate
3 Days (4.5 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
AI/ML for Scientific Discovery Using PyTorch and JAX is a professional training program that introduces learners to artificial intelligence and machine learning techniques for modern scientific research. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
AI/ML for Scientific Discovery Using PyTorch and JAX is a professional training program that introduces learners to artificial intelligence and machine learning techniques for modern scientific research.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• 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
Day 1 – Scientific ML Foundations & PyTorch‑Based Property Prediction
Explore AI/ML roles in scientific discovery and inverse design • Prepare and visualize scientific datasets (molecular, materials, simulation) • Implement tensor representations and automatic differentiation in PyTorch • Design neural networks for property prediction
Day 2 – Physics‑Informed Neural Networks & Surrogate Modeling
Integrate physics‑based loss functions into neural networks • Encode differential equations, boundary conditions, and conservation laws • Build surrogate models for expensive simulations • Perform parameter estimation and inverse modeling
Day 3 – JAX for Differentiable Scientific Computing & Optimization
Utilize JAX transformations (grad, jit, vmap) for high‑performance ML • Create differentiable scientific computing pipelines • Optimize parameters with Optax‑based workflows • Compare PyTorch and JAX for research workloads
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | JAX |
| Covered Tool / Platform | Optax |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | SciPy |
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