# Maximally predictive ensemble dynamics from data

@article{Costa2021MaximallyPE, title={Maximally predictive ensemble dynamics from data}, author={Antonio Carlos Costa and Tosif Ahamed and David J. Jordan and Greg J. Stephens}, journal={bioRxiv}, year={2021} }

We leverage the interplay between microscopic variability and macroscopic order to connect physical descriptions across scales directly from data, without underlying equations. We reconstruct a state space by concatenating measurements in time, building a maximum entropy partition of the resulting sequences, and choosing the sequence length to maximize predictive information. Trading non-linear trajectories for linear, ensemble evolution, we analyze reconstructed dynamics through transfer…

## 2 Citations

Chaos as an interpretable benchmark for forecasting and data-driven modelling

- Computer ScienceNeurIPS Datasets and Benchmarks
- 2021

A growing database currently comprising 131 known chaotic dynamical systems spanning continents such as astro-physics, climatology, and biochemistry is presented, paired with precomputed multivariate and univariate time series.

Processive and Distributive Non-Equilibrium Networks Discriminate in Alternate Limits

- Biology
- 2020

This work shows that for a general class of proofreading networks, energetic discrimination requires processivity and kinetic discrimination requiring distributivity, and shows that mixed networks, in which one product is favored energetically and the other kinetically, are introduced.

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