Homotopy-based training of NeuralODEs for accurate dynamics discovery

4 October 2022

Papers citing "Homotopy-based training of NeuralODEs for accurate dynamics discovery"

8 / 8 papers shown

Title
A scalable generative model for dynamical system reconstruction from neuroimaging data Eric Volkmann Alena Brändle Daniel Durstewitz G. Koppe AI4CE 28 1 0 05 Nov 2024
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction Manuel Brenner Christoph Jurgen Hemmer Zahra Monfared Daniel Durstewitz AI4CE 33 0 0 18 Oct 2024
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data Manuel Brenner Elias Weber G. Koppe Daniel Durstewitz AI4TS AI4CE 36 3 0 07 Oct 2024
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction Christoph Jurgen Hemmer Manuel Brenner Florian Hess Daniel Durstewitz 36 3 0 07 Jun 2024
Out-of-Domain Generalization in Dynamical Systems Reconstruction Niclas Alexander Göring Florian Hess Manuel Brenner Zahra Monfared Daniel Durstewitz AI4CE 32 10 0 28 Feb 2024
Moderate Adaptive Linear Units (MoLU) Hankyul Koh Joon-Hyuk Ko W. Jhe 13 0 0 27 Feb 2023
On the difficulty of learning chaotic dynamics with RNNs Jonas M. Mikhaeil Zahra Monfared Daniel Durstewitz 59 50 0 14 Oct 2021
Multiple shooting for training neural differential equations on time series Evren Mert Turan J. Jäschke AI4TS 37 23 0 14 Sep 2021