mlsanityData to 7 Oct 2026

Neural Solvers for Fast and Accurate Numerical Optimal Control

arXiv 2203.08072 ↗First posted 13 Mar 2022math.OC cs.LGcited by 4

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Abstract (arXiv)

Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numerical methods that can be applied: computationally expensive but accurate numerical routines are replaced by fast and inaccurate methods, trading inference time for solution accuracy. This paper provides techniques to improve the quality of optimized control policies given a fixed computational budget. We achieve the above via a hypersolvers approach, which hybridizes a differential equation solver and a neural network. The performance is evaluated in direct and receding-horizon optimal control tasks in both low and high dimensions, where the proposed approach shows consistent Pareto improvements in solution accuracy and control performance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 43 references that are in the map. First posted on arXiv 13 Mar 2022.

Builds on

16 of 43 references.

References · 16

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4 more
  1. 2021Data-Driven MPC for Quadrotorscited by 32
  2. 2021Differentiable Multiple Shooting Layerscited by 20
  3. 2020TorchDyn: A Neural Differential Equations Librarycited by 16
  4. 2015Optimal Control and Coordination of Connected and Automated Vehicles at Urban Traffic Intersectionscited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.

4 papers.

  1. 2022Transform Once: Efficient Operator Learning in Frequency Domaincited by 20
  2. 2022Learning context-aware adaptive solvers to accelerate quadratic programmingcited by 7
  3. 2023Learning Efficient Surrogate Dynamic Models with Graph Spline Networkscited by 2
  4. 2025Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flowscited by 1