New Method Automatically Detects and Resolves Model Degeneracies

In scientific modeling and machine learning, "degeneracies" occur when multiple parameters or labels produce indistinguishable data, making both prediction and inverse problems challenging. These degeneracies hinder algorithms that rely on clear distinctions between data and parameter gradients. Identifying them, however, can offer profound insights into the underlying physical processes or model choices. Researchers introduce the "degeneracy distillery," a method designed to automatically and symbolically detect and resolve these degenerate parameter combinations. It operates solely on parameter-data pairs, estimating and flattening the Fisher information matrix to explore the information geometry of the likelihood function. This approach characterizes degeneracies as an intrinsic property of the physical model, independent of observed data. The method discovers symbolic coordinate transformations that reveal which parameter combinations independently affect the data. These transformations globally flatten the Fisher information, significantly reducing the simulation budget required for downstream neural posterior estimation—up to 10 times fewer simulations in test cases. This not only boosts efficiency but also provides deeper physical insight into the system being modeled.