Reconstructing computational system dynamics from neural data with recurrent neural networks
Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …
equations. The behaviour of such systems is the subject of dynamical systems theory …
Temporal subsampling diminishes small spatial scales in recurrent neural network emulators of geophysical turbulence
The immense computational cost of traditional numerical weather and climate models has
sparked the development of machine learning (ML) based emulators. Because ML methods …
sparked the development of machine learning (ML) based emulators. Because ML methods …
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
BB Sorensen, L Zepeda-Núñez, I Lopez-Gomez… - arXiv preprint arXiv …, 2024 - arxiv.org
Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering.
However, their study remains a challenge due to the large range scales, and the strong …
However, their study remains a challenge due to the large range scales, and the strong …
Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
Invariant measures are widely used to compare chaotic dynamical systems, as they offer
robustness to noisy data, uncertain initial conditions, and irregular sampling. However, large …
robustness to noisy data, uncertain initial conditions, and irregular sampling. However, large …
Understanding Learning through the Lens of Dynamical Invariants
A Ushveridze - arXiv preprint arXiv:2401.10428, 2024 - arxiv.org
This paper proposes a novel perspective on learning, positing it as the pursuit of dynamical
invariants--data combinations that remain constant or exhibit minimal change over time as a …
invariants--data combinations that remain constant or exhibit minimal change over time as a …