Imagen de Portada

Optimal Control-Based Falsification of Learnt Dynamics via Neural ODEs and Symbolic Regression

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kötz, Lasse, Sjöberg, Jonas, Åkesson, Knut
Formato:	Preprint
Publicado:	2026
Materias:	Systems and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Learning Neural Hybrid Surrogates for Gradient-Based Falsification
por: Kötz, Lasse, et al.
Publicado: (2026)

Falsification of Cyber-Physical Systems using Bayesian Optimization
por: Ramezani, Zahra, et al.
Publicado: (2022)

Gradient Field-Based Dynamic Window Approach for Collision Avoidance in Complex Environments
por: Zhang, Ze, et al.
Publicado: (2025)

Combining High Level Scheduling and Low Level Control to Manage Fleets of Mobile Robots
por: Roselli, Sabino Francesco, et al.
Publicado: (2025)

Collision-Free Navigation of Mobile Robots via Quadtree-Based Model Predictive Control
por: Ali, Osama Al Sheikh, et al.
Publicado: (2025)

Interaction-Aware Predictive Environmental Control Barrier Function for Emergency Lane Change
por: Quan, Ying Shuai, et al.
Publicado: (2026)

Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
por: Zhang, Ze, et al.
Publicado: (2025)

Efficient Verification and Falsification of ReLU Neural Barrier Certificates
por: Ren, Dejin, et al.
Publicado: (2025)

Certified Robust Invariant Polytope Training in Neural Controlled ODEs
por: Harapanahalli, Akash, et al.
Publicado: (2024)

Expressivity of Quadratic Neural ODEs
por: Hanson, Joshua, et al.
Publicado: (2025)

AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control
por: Yu, Shao-Yi, et al.
Publicado: (2025)

ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence
por: Mei, Wenjie, et al.
Publicado: (2024)

Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach
por: Shahrooei, Zahra, et al.
Publicado: (2024)

Rademacher Complexity of Neural ODEs via Chen-Fliess Series
por: Hanson, Joshua, et al.
Publicado: (2024)

Falsification of Autonomous Systems in Rich Environments
por: Elimelech, Khen, et al.
Publicado: (2024)

Reinforcement Learning Based Symbolic Regression for Load Modeling
por: Lin, Ding, et al.
Publicado: (2025)

Agile Temporal Discretization for Symbolic Optimal Control
por: Janssens, Adrien, et al.
Publicado: (2025)

Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
por: Shi, Kanghong, et al.
Publicado: (2025)

Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications
por: Prignoli, Francesco, et al.
Publicado: (2025)

Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics
por: Wang, Xinyao, et al.
Publicado: (2026)

From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
por: Cipriani, Cristina, et al.
Publicado: (2023)

Quantitative Flow Approximation Properties of Narrow Neural ODEs
por: Elamvazhuthi, Karthik
Publicado: (2025)

Falsification-driven reinforcement learning for maritime motion planning
por: Müller, Marlon, et al.
Publicado: (2025)

Data-Driven Dynamics Modeling of Miniature Robotic Blimps Using Neural ODEs With Parameter Auto-Tuning
por: Zhu, Yongjian, et al.
Publicado: (2024)

Data-driven discovery and control of multistable nonlinear systems and hysteresis via structured Neural ODEs
por: Salas, Ike Griss, et al.
Publicado: (2026)

Data-driven HVAC Control Using Symbolic Regression: Design and Implementation
por: Ozawa, Yuki, et al.
Publicado: (2023)

Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimation
por: Sundaram, Ganesh, et al.
Publicado: (2026)

Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
por: Liu, Dechuan, et al.
Publicado: (2026)

Modeling Power Systems Dynamics with Symbolic Physics-Informed Neural Networks
por: Tran, Huynh T. T., et al.
Publicado: (2023)

Optimal Inferential Control of Convolutional Neural Networks
por: Vaziri, Ali, et al.
Publicado: (2024)

Pontryagin Optimal Control via Neural Networks
por: Gu, Chengyang, et al.
Publicado: (2022)

Physics-Informed Graph Neural Jump ODEs for Cascading Failure Prediction in Power Grids
por: Sevak, Birva, et al.
Publicado: (2026)

Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees
por: Nawaz, Farhad, et al.
Publicado: (2023)

Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery
por: Feng, Jie, et al.
Publicado: (2024)

Optimality Robustness in Koopman-Based Control
por: Lin, Yicheng, et al.
Publicado: (2026)

Optimal Functional Incentives for Control: The Linear-Quadratic Case with Bilinear Incentives
por: Matt, Jonas G., et al.
Publicado: (2026)

Robust Convolution Neural ODEs via Contractivity-promoting regularization
por: Zakwan, Muhammad, et al.
Publicado: (2025)

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
por: Lederer, Armin, et al.
Publicado: (2019)

Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation
por: Hu, Hanjiang, et al.
Publicado: (2024)

A Review on Symbolic Regression in Power Systems: Methods, Applications, and Future Directions
por: Javadi, Amir Bahador, et al.
Publicado: (2025)