A framework of fine-tuned LLMs autoformalizes PDE control problems into signal temporal logic, generates Gurobi solver code, and proposes subgoal constraints; it beats generic LLMs in decoupled tests but shows mixed end-to-end utility gains.
The purpose is to reduce the second-order derivatives of u to first derivatives so that we can obtain (linear) approximations to u
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PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs
A framework of fine-tuned LLMs autoformalizes PDE control problems into signal temporal logic, generates Gurobi solver code, and proposes subgoal constraints; it beats generic LLMs in decoupled tests but shows mixed end-to-end utility gains.