ArticleNature communications2022
AI Pontryagin or how artificial neural networks learn to control dynamical systems.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed.
- Optimising pandemic response through vaccination strategies using neural networks.Scientific reports · 2026Article
- From Decay to Rhythm: Coherent Biological Oscillators Require More Than Chemistry Alone.bioRxiv : the preprint server for biology · 2026Article
- High-order expansion of neural ordinary differential equation flows.Science advances · 2025Article
- Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations.PLoS computational biology · 2025Article
- Solving nonlinear and complex optimal control problems via multi-task artificial neural networks.Scientific reports · 2025Article
- Learning interpretable network dynamics via universal neural symbolic regression.Nature communications · 2025Article
- Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Optimal control of agent-based models via surrogate modeling.PLoS computational biology · 2025Article
- Accelerated optimization in deep learning with a proportional-integral-derivative controller.Nature communications · 2024Article
- Article
- On fast simulation of dynamical system with neural vector enhanced numerical solver.Scientific reports · 2023Article
- Deep Reinforcement Meta-Learning and Self-Organization in Complex Systems: Applications to Traffic Signal Control.Entropy (Basel, Switzerland) · 2023Article
- A Refunding Scheme to Incentivize Narrow-Spectrum Antibiotic Development.Bulletin of mathematical biology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. To overcome this outstanding challenge, we present AI Pontryagin, a versatile control framework based on neural ordinary differential equations that automatically learns control signals that steer high-dimensional dynamical systems towards a desired target state within a specified time interval. We demonstrate the ability of AI Pontryagin to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that AI Pontryagin is capable of solving a wide range of control and optimization problems, including those that are analytically intractable.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.