# Analysis of sloppiness in model simulations: unveiling parameter uncertainty when mathematical models are fitted to data

arXiv: https://arxiv.org/abs/2203.15184 · HTML: https://mlsanity.com/paper/2203.15184/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model](https://mlsanity.com/paper/2312.17336/) (2023)
- [Ill-Conditioning in Dictionary-Based Dynamic-Equation Learning: A Systems Biology Case Study](https://mlsanity.com/paper/2603.11330/) (2026)
- [Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms](https://mlsanity.com/paper/2307.07134/) (2023)
- [Fast and Robust Simulation-Based Inference With Optimization Monte Carlo](https://mlsanity.com/paper/2511.13394/) (2025)

## Abstract (arXiv)

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This novel approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.