Overview
Part III covers the data-driven and model-driven techniques at the heart of computational discovery. From exploratory analysis and representation learning through foundation models and reasoning architectures, these chapters show how modern ML finds anomalies, infers causal structure, and generates novel hypotheses. The part also addresses Bayesian methods, physics-informed scientific ML, generative models, and symbolic regression for recovering interpretable laws from data.
Chapters
Chapter 25
Exploratory Discovery
Unsupervised exploration, clustering, dimensionality reduction, and data-driven hypothesis generation.
Chapter 26
Representation Learning
Learning meaningful embeddings and latent spaces that capture the structure of scientific data.
Chapter 27
Scientific Foundation Models
Large pretrained models for proteins, molecules, climate, and other scientific domains.
Chapter 28
Multimodal Scientific AI
Integrating text, images, spectra, sequences, and graphs for cross-modal scientific reasoning.
Chapter 29
Reasoning Models
Chain-of-thought, tree search, and neuro-symbolic architectures for structured scientific reasoning.
Chapter 30
Anomaly and Novelty Detection
Identifying unexpected patterns, outliers, and novel phenomena that signal potential discoveries.
Chapter 31
Causal Discovery
Learning causal graphs from observational and interventional data to explain mechanisms.
Chapter 32
Bayesian Discovery
Probabilistic modeling, Bayesian optimization, and uncertainty-aware discovery workflows.
Chapter 33
Scientific Machine Learning
Physics-informed neural networks, neural operators, and hybrid models that respect physical laws.
Chapter 34
Generative Models for Discovery
VAEs, diffusion models, and flow-based architectures for generating molecules, materials, and designs.
Chapter 35
Symbolic Regression
Recovering interpretable mathematical expressions and physical laws directly from data.