Hands-on with PINNs, Generative Models and Autonomous R&D

This workshop aims to introduce participants to the practical applications of Artificial Intelligence in Applied Science and help them understand how AI can be used for scientific data analysis, prediction, automation, and real-world problem-solving.

   Day 1: Physics-Informed & Geometric Deep Learning

Integrating scientific domain knowledge into neural architectures to ensure physical consistency and data efficiency.

• The Inductive Bias Shift: Moving from black-box models to architectures that respect conservation laws such as mass, energy, and momentum.
• Physics-Informed Neural Networks: Implementing Partial Differential Equations directly into the loss function.
• Geometric Deep Learning: Exploring equivariant and invariant networks for 3D spatial data, molecular structures, and fluid manifolds.

Advanced Hands-on: Google Colab

• Project: Developing a PINN to simulate fluid flow or heat transfer.
• Key Skill: Utilizing automatic differentiation to enforce physical constraints without needing massive labeled datasets.

  Day 2: Generative Models & Autonomous R&D Pipelines

Accelerating the research cycle through generative design and closed-loop experimental optimization.

• Generative AI in Science: Applications of Diffusion Models and Variational Autoencoders in crystal structure prediction and protein design.
• Bayesian Optimization & Active Learning: Building self-driving labs that intelligently select the next experiment to perform.
• Scientific LLM Agents: Leveraging multi-modal Large Language Models to automate literature synthesis and hypothesis generation.

Advanced Hands-on: Google Colab

• Project: Targeted Molecular Design.
• Key Skill: Training a latent diffusion model to generate novel chemical structures with optimized properties such as specific conductivity or binding affinity using RDKit.

   Day 3: Explainability, Uncertainty & Symbolic Discovery

Ensuring reliability and extracting human-readable insights from high-dimensional AI models.

• Uncertainty Quantification: Utilizing Conformal Prediction and Bayesian inference to measure model confidence for high-stakes industrial deployment.
• Symbolic Regression: Transforming neural outputs into human-readable mathematical formulas.
• Model Interpretability: Exploring advanced XAI techniques tailored for scientific data, including attribution maps and structural causal models.

Advanced Hands-on: Google Colab

• Project: AI-to-Formula Discovery.
• Key Skill: Implementing PySR to rediscover physical laws from noisy experimental sensor data, bridging the gap between deep learning and classical theory.

Technical Implementation & Environment

• Platform: All modules are optimized for Google Colab and Jupyter Notebooks, utilizing GPU-accelerated libraries.
• Toolkit: Participants will work with industry-standard frameworks including PyTorch, DeepXDE, RDKit, and PySR.
• Outcome: Participants will leave with a portfolio of advanced notebooks that can be adapted for real-world R&D challenges in chemistry, physics, engineering, and pharmacology.