An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
Automating turbulence modelling by multi-agent reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DARSM embeds a neural network inside an implicit algebraic Reynolds stress model derived from transport equations, trains it end-to-end via adjoint PDE optimization, and reports 2-4x average velocity error reduction plus generalization from attached to separated flows on duct and hill benchmarks.
citing papers explorer
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Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
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Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
DARSM embeds a neural network inside an implicit algebraic Reynolds stress model derived from transport equations, trains it end-to-end via adjoint PDE optimization, and reports 2-4x average velocity error reduction plus generalization from attached to separated flows on duct and hill benchmarks.