logo
ResearchBunny Logo
Abstract
This paper introduces Al Pontryagin, a neural ordinary differential equation (NODE)-based control framework that learns control signals for steering high-dimensional dynamical systems toward desired target states within specific time intervals. It demonstrates Al Pontryagin's ability to learn control signals closely resembling those from optimal control frameworks, showcasing its potential for solving analytically and computationally intractable control and optimization problems.
Publisher
NATURE COMMUNICATIONS
Published On
Jan 17, 2022
Authors
Lucas Böttcher, Nino Antulov-Fantulin, Thomas Asikis
Tags
neural ordinary differential equations
control framework
dynamical systems
optimization problems
machine learning
control signals

Related Publications

Explore these studies to deepen your understanding of the subject.

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation
Computer Science

Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation

S. Idrees, M. B. Manookin, et al.

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
Computer Science

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

C. H. Martin, T. (. Peng, et al.

Modeling transcriptomic age using knowledge-primed artificial neural networks
Medicine and Health

Modeling transcriptomic age using knowledge-primed artificial neural networks

N. Holzscheck, C. Falckenhayn, et al.

Competing instabilities reveal how to rationally design and control active crosslinked gels
Physics

Competing instabilities reveal how to rationally design and control active crosslinked gels

B. Najma, M. Varghese, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny