Anthropic Science Blog details how AI agents build and constrain scientific models

Anthropic published a Science Blog post on 6 November 2026 by AI for Science grantees Jakob Macke, Daniel Gedon, and Stefan Wahl of the ELLIS Institute Tübingen and the University of Tübingen. Tracing scientific modelling from the 1951 Hodgkin-Huxley squid axon equations to modern computational neuroscience, the authors break the discipline into three recurring steps: proposing model equations, constraining parameters against experimental data, and interpreting what the equations imply about physical mechanisms.

Where classical Bayesian fitting struggles on complex simulators, simulation-based inference (SBI) runs a simulator 100,000 to millions of times with varied parameters and trains a neural network to return full posterior probability distributions in seconds. Pairing SBI with language models that write, run, and revise simulator code lets researchers search across families of candidate models rather than settling on a single manual curve fit.