Here we provide eigenvalues for the cost Hessian (in statistics, the eigenvalues of $\chi^2$) at the best fit, for a series of models. (For probabilistic models like the Ising model and the &Lambda CDM model for the cosmic microwave background radiation, these are eigenvalues of the Fisher Information matrix, a generalization of the cost Hessian to probabilistic models.)

- Cell signaling
- Radioactive decay (sums of exponentials)
- Variational wavefunction for Quantum Monte Carlo
- Cosmic microwave background radiation
- Discrete diffusion model
- 2D Ising model
- Meat oxidation model
- Model for particle accelerator
- Neural network number 1
- van der Pol oscillator
- Circadian clock
- Combustion network
- Neural network number 2
- Stock price time series distribution
- Model for particle accelerator (Energy Recovery Linac)
- Hessian for the emittance (not a least-squares cost) for the CESR particle accelerator (CESR), and the Hessian.
- Model of the H2/O2 combustion mechanism
- Neuroscience Hodgkin Huxley model of action potential
- Power systems model of the IEEE 14-bus test system
- Stillinger-Weber interatomic potential for a monolayer of MoS2
- Bryan Daniels' model of gravity generated by SirIsaac
- Transmission loss in an underwater environment with a clay bottom

- Sloppy Models
- A sloppy systems biology model
- What is Sloppiness?
- What are Sloppy Models?
- Fitting Exponentials: Prediction without parameters
- Fitting Polynomials: Where is sloppiness from?
- Why sloppiness? The Sloppy Universality Class
- Differential Geometry and Sloppy Models (Transtrum)
- The Model Manifold and Hyperribbons (Transtrum)
- Sloppy Curvature (Transtrum)
- Model Manifold Comparisons of Algorithms (Kloumann)

- Why is science possible? Sloppy models in Physics.
- Jessie Silverberg's Huffington Post article and Katheryn McGill's vlog Interview from The Physics Factor.
- Unedited workshop interview by Steven Reiner, Stony Brook School of Journalism; Mobile version.

- Sloppy model applications
- Do parameters matter? Fits versus measurements.
- Experimental design in sloppy systems
- Robustness and sloppiness
- Estimating systematic errors for interatomic potentials and for density functional theory.
- Learning digits with InPCA

Last Modified: Sept 16, 2020

James P. Sethna, sethna@lassp.cornell.edu; This work supported by the Division of Materials Research of the U.S. National Science Foundation, through grant DMR-070167.

Statistical Mechanics: Entropy, Order Parameters, and Complexity, now available at Oxford University Press (USA, Europe).