Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches

10 October 2023

Papers citing "Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches"

7 / 7 papers shown

Title
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Jake Buzhardt C. Ricardo Constante-Amores Michael D. Graham 58 2 0 20 Nov 2024
Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD Shahriar Akbar Sakib Shaowu Pan 24 0 0 13 Aug 2024
Machine Learning based Prediction of Ditching Loads Henning Schwarz Micha Überrück J. Zemke Thomas Rung MU AI4CE 10 1 0 16 Feb 2024
Liquid Resistance Liquid Capacitance Networks Mónika Farsang Sophie A. Neubauer Radu Grosu AI4TS 18 2 0 30 Jan 2024
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder Priyam Gupta Peter J. Schmid D. Sipp T. Sayadi Georgios Rigas 14 4 0 16 Oct 2023
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories Samuel E. Otto Sebastian Peitz C. Rowley 29 19 0 20 Sep 2022
The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems Matthew J. Colbrook 23 34 0 06 Sep 2022