📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI/ML for Scientific Discovery Using PyTorch and JAX, from foundations to a certified capstone project.
Outline
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
Outline
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
Outline
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
Earn government-registered certification in AI/ML for Scientific Discovery Using PyTorch and JAX
e-Certificate and e-Marksheet issued on successful completion.
