• Understand the role of AI and machine learning in scientific discovery.
• Learn the fundamentals of deep learning workflows using PyTorch and JAX.
• Apply AI/ML models to scientific datasets and research problems.
• Understand how neural networks can be used for prediction, classification, regression, and pattern discovery.
• Explore JAX for high-performance numerical computing and differentiable programming.
• Build basic AI/ML pipelines for scientific data processing and model development.
• Understand the importance of model evaluation, reproducibility, and responsible AI in research.
• Gain exposure to real-world scientific applications of AI/ML across interdisciplinary domains.

Day 1: Scientific ML Foundations & PyTorch-Based Property Prediction

• AI/ML for scientific discovery, simulation acceleration, and inverse design
• Scientific datasets: molecular, materials, physics, experimental, and simulation data
• Scientific data challenges: small datasets, noise, sparsity, uncertainty, and extrapolation
• Tensor representation of scientific systems using PyTorch
• Automatic differentiation for scientific modeling
• Designing neural networks for scientific property prediction
• Model validation, residual analysis, and scientific interpretability

Hands-on

Day 2: Physics-Informed Neural Networks & Surrogate Modeling

• Physics-informed neural networks
• Scientific loss functions: data loss, physics loss, constraint loss
• Incorporating differential equations, boundary conditions, and conservation laws
• Surrogate models for expensive simulations
• Parameter estimation and inverse modeling
• Failure modes: non-physical predictions, overfitting, and unstable training
• Applications in diffusion, heat transfer, reaction kinetics, and material response modeling

Hands-on

Day 3: JAX for Differentiable Scientific Computing & Optimization

• JAX for high-performance scientific ML
• Key transformations: grad, jit, and vmap
• Differentiable scientific computing and parameter optimization
• Vectorized and accelerated scientific model training
• Optax-based optimization workflows
• Comparing PyTorch and JAX for research applications
• Advanced trends: graph neural networks, neural operators, equivariant models, foundation models, and AI-driven materials discovery

Hands-on

• Explain how AI/ML accelerates scientific research and innovation.
• Use PyTorch to design, train, and evaluate machine learning/deep learning models.
• Use JAX for numerical computing, automatic differentiation, and scientific machine learning workflows.
• Prepare and process scientific datasets for AI/ML-based analysis.
• Develop basic neural network models for scientific prediction and discovery tasks.
• Interpret model outputs and evaluate model performance using appropriate metrics.
• Apply AI/ML approaches to interdisciplinary research problems.
• Build confidence in using modern AI frameworks for research, experimentation, and scientific computing.

• Students pursuing science, engineering, computer science, biotechnology, physics, chemistry, mathematics, or related fields
• Ph.D. scholars and research scholars working with scientific or computational data
• Faculty members and academicians interested in AI-driven research
• Industry professionals working in R&D, data science, scientific computing, or applied AI
• Researchers interested in applying PyTorch, JAX, and machine learning to scientific discovery