mlsanityData to 14 Oct 2026

Learning for CasADi: Data-driven Models in Numerical Optimization

arXiv 2312.05873 ↗First posted 10 Dec 2023eess.SY cs.AI cs.LGcited by 15

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Abstract (arXiv)

While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi facilitate seamless usage of solvers but face challenges when integrating learned process models into numerical optimizations. To address this gap, we present the Learning for CasADi (L4CasADi) framework, enabling the seamless integration of PyTorch-learned models with CasADi for efficient and potentially hardware-accelerated numerical optimization. The applicability of L4CasADi is demonstrated with two tutorial examples: First, we optimize a fish's trajectory in a turbulent river for energy efficiency where the turbulent flow is represented by a PyTorch model. Second, we demonstrate how an implicit Neural Radiance Field environment representation can be easily leveraged for optimal control with L4CasADi. L4CasADi, along with examples and documentation, is available under MIT license at https://github.com/Tim-Salzmann/l4casadi

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 10 Dec 2023.

Builds on

9 of 21 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 664
  3. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  4. 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
  5. 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 161
  6. 2019acados: a modular open-source framework for fast embedded optimal controlcited by 68
  7. 2022Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platformscited by 41
  8. 2022Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPCcited by 5
  9. 2022Zero-Order Optimization for Gaussian Process-based Model Predictive Controlcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 5.

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  1. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 26
  2. 2025A view on learning robust goal-conditioned value functions: Interplay between RL and MPCcited by 7
  3. 2026Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesiscited by 7
  4. 2024AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Controlcited by 6
  5. 2025MPCritic: A plug-and-play MPC architecture for reinforcement learningcited by 3
  6. 2024Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practicecited by 1
  7. 2025Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learningcited by 1
  8. 2024Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objectivecited by 0
  9. 2024Autonomous Wheel Loader Navigation Using Goal-Conditioned Actor-Critic MPCcited by 0
  10. 2024Dynamic Neural Potential Field: Online Trajectory Optimization in the Presence of Moving Obstaclescited by 0
  11. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agentscited by 0
  12. 2026White-Box Neural Ensemble for Vehicular Plasticity: Quantifying the Efficiency Cost of Symbolic Auditability in Adaptive NMPCcited by 0
3 more
  1. 2026T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamicscited by 0
  2. 2026Language-Guided Terrain-Adaptive Neural MPC for Autonomous Traversal of Articulated Tracked Robotscited by 0
  3. 2026Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurementscited by 0