arXiv AI

Self-Evolving Scientific Agent Designs Physically Reasoned White-Box Fluid Control

The paper presents a method where self‑evolving scientific agents design explicit, neural‑network‑free white‑box controllers for fluid dynamics tasks. By iteratively interpreting simulation data, the agents accumulate control knowledge and refine controller code, ultimately achieving robust control of an underactuated two‑joint swimmer in unsteady flows. The resulting controllers generalize across varying target positions, wake geometries, cylinder counts, and inflow speeds, and 2D control priors successfully transfer to accelerate 3D adaptation.

arXiv AI

Aug 26

Self-Evolving Scientific Agent Designs Physically-Reasoned Whitebox Fluid Control

The paper introduces a self‑evolving scientific agent that uses large language models and iterative code generation to build interpretable, physically‑reasoned white‑box controllers for complex systems. The agent deploys candidate controllers in simulations, diagnoses dynamic behavior from multimodal evidence, and refines source code until a robust policy is achieved. Applied to a nonlinear fluid‑structure interaction problem—a two‑joint dogfish swimmer navigating an unsteady wake—the agent autonomously designs a controller that consistently reaches targets across a wide range of conditions without retraining.

By Boai Sun, Wenjin Guo, Zongmin Yu, Liu Yang

llms agents reinforcement-learning multimodal safety

arXiv AI

Jun 9

Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control

arXiv:2606. 08405v1 Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures.

By Boai Sun, Wenjin Guo, Zongmin Yu, Liu Yang

llms agents reinforcement-learning multimodal safety

arXiv AI

Jul 1

The HydroGym Reinforcement Learning Platform for Fluid Dynamics

arXiv:2512. 17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering.

By Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario R\"uttgers, Yuning Wang, Pol Su\'arez, Ludger Paehler, Deniz A. Bezgin, Aaron B. Buhendwa, Jared L. Callaham, Samuel Ahnert, Nicholas Zolman, Xiao Shao, Jean-Christophe Loiseau, Nikolaus Adams, Matthias Meinke, Wolfgang Schr\"oder, Kai Lagemann, Esther Lagemann, Ricardo Vinuesa, Steven L. Brunton

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 16

Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning

arXiv:2607. 13553v1 Announce Type: cross Abstract: Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments.

By Andrea Maria Braghin, Nicol\`o Botteghi, Matteo Tomasetto, Andrea Manzoni, Gabriele Cazzulani

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 26

Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries

arXiv:2606. 26154v1 Announce Type: cross Abstract: Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an open challenge.

By Jannik Drotleff, Samuel Tovey, Paul Hohenberger, Christoph Lohrmann, Julian Ho{\ss}bach, Konstantin Nikolaou, Christian Holm

agents reinforcement-learning robotics efficiency

arXiv AI

Jul 14

Heuristic Learning for Active Flow Control Using Coding Agents

arXiv:2607. 11565v1 Announce Type: cross Abstract: Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging.

By Paul Garnier, Jonathan Viquerat, Elie Hachem

agents reinforcement-learning efficiency benchmarks

Hugging Face Trending Papers

Jul 2

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment using a single policy.

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 3

Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning

arXiv:2607. 02037v1 Announce Type: cross Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment.

By Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea, Aswin Ramachandran

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Aug 11

Neural Operators for Immersed-Boundary Soft Swimmers Locomotion

arXiv:2608. 07722v1 Announce Type: new Abstract: High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engineering design, parameter studies, and control.

By Mohammad Sadegh Eshaghi, Yizheng Wang, Navid Valizadeh, Xiaoying Zhuang, Timon Rabczuk

robotics

arXiv Machine Learning

Aug 21

Complementary, Not Cumulative: Interaction Effects in Physics-Informed Neural Networks for Navier-Stokes Vortex Shedding

arXiv:2608. 19632v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) embed governing partial differential equations directly into the training loss, offering a promising alternative to costly CFD solvers for unsteady flows.

By Devesh Shah

benchmarks

arXiv AI

Jul 21

Generalist AI Control: Towards Multi-purpose Adaptive Algorithms

arXiv:2607. 16313v1 Announce Type: new Abstract: Traditional controllers are designed for specific systems and do not transfer across different system orders and dynamics.

By Klinsmann Agyei, Pouria Sarhadi

agents

arXiv Machine Learning

Jun 9

Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning

arXiv:2606. 08513v1 Announce Type: cross Abstract: Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control.

By Elisei Shafer, Oren Gal

agents reinforcement-learning robotics

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