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

2026-06-06

AI Summary

This work addresses the challenge of simultaneously achieving interpretability, physical consistency, and generalization in complex fluid control tasks by introducing a large language model–driven, self-evolving scientific agent. The proposed method iteratively generates code and interacts with multimodal physics simulators to autonomously evolve human-readable, traceable symbolic control policies—without requiring weight tuning. Applied to fluid–structure interaction systems, the agent discovers a unified controller incorporating mechanisms such as traveling-wave propulsion and body-frame guidance, substantially enhancing cross-task generalization. Experiments demonstrate that the synthesized controller robustly executes tasks on unseen static targets and dynamic trajectories, while remaining fully auditable and mathematically interpretable throughout the entire process.

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. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and rigorous physical reasoning. Instead of adjusting weights, the agent deploys candidate strategies into physical simulations, actively diagnoses dynamic behaviors from multimodal evidence, and translates these observations into progressive source-code refinements. We demonstrate this framework on a highly non-linear fluid-structure interaction problem: an underactuated, two-joint dogfish swimmer tasked with spatial target reaching using only joint angular accelerations. Starting from a propulsive seed policy that exhibits a one-sided steering bias, the agent autonomously discovers and refines a unified controller that robustly captures all canonical targets. Remarkably, without any retraining or target-specific branching, the synthesized control policy generalizes to unseen static targets and dynamically curved pursuit trajectories. The auditable evolve log reveals an emergent control architecture built upon traveling-wave propulsion, body-frame target guidance, yaw-rate feedback, signed mean-tail curvature, and adaptive cadence relief. Our results show that an autonomous scientific agent can successfully transform accumulated physical evidence into robust, mathematically readable control policy, while maintaining a fully traceable process of scientific discovery.

Problem

• fluid-structure interaction
• interpretable control
• physical reasoning
• generalizable control
• autonomous scientific discovery

Innovation

• self-evolving agent
• physically-reasoned control
• interpretable reinforcement learning
• fluid-structure interaction
• code-based policy synthesis