Overview
Part V explores how simulation and optimization accelerate the discovery loop. Differentiable programming makes entire simulation pipelines trainable end-to-end, while world models learn to predict system dynamics from data. The chapters cover optimization techniques for navigating vast design spaces, principled experiment design that maximizes information gain per trial, and experiment registries that ensure reproducibility across discovery campaigns.
Chapters
Chapter 42
Differentiable Programming
Making simulations and computational pipelines end-to-end differentiable for gradient-based discovery.
Chapter 43
Scientific Simulation
AI-accelerated numerical simulation, surrogate models, and learned solvers for scientific computing.
Chapter 44
World Models
Learning predictive models of system dynamics for planning, counterfactual reasoning, and exploration.
Chapter 45
Optimization for Discovery
Black-box, multi-objective, and combinatorial optimization methods for navigating design spaces.
Chapter 46
Experiment Design
Active learning, Bayesian experimental design, and adaptive strategies that maximize information per trial.
Chapter 47
Experiment Registries
Tracking experiments, results, and metadata to ensure reproducibility across discovery campaigns.