A Blueprint for Self-Evolving Coding Agents in Vehicle Aerodynamic Drag Prediction

TL;DR

This paper introduces a self-evolving, contract-based framework for surrogate modeling in vehicle drag prediction, improving efficiency, reliability, and governance in aerodynamic design workflows.

Contribution

It presents a novel program-based surrogate discovery method combining evolutionary algorithms, evaluator feedback, and strict contracts for industrial vehicle aerodynamics.

Findings

Achieved a Combined Score of 0.9335 with sign-accuracy of 0.9180.

Adaptive sampling and island migration significantly improve convergence.

The workflow enables reliable, traceable, and accelerated aerodynamic design iterations.

Abstract

High-fidelity vehicle drag evaluation is constrained less by solver runtime than by workflow friction: geometry cleanup, meshing retries, queue contention, and reproducibility failures across teams. We present a contract-centric blueprint for self-evolving coding agents that discover executable surrogate pipelines for predicting drag coefficient $C_d$ under industrial constraints. The method formulates surrogate discovery as constrained optimization over programs, not static model instances, and combines Famou-Agent-style evaluator feedback with population-based island evolution, structured mutations (data, model, loss, and split policies), and multi-objective selection balancing ranking quality, stability, and cost. A hard evaluation contract enforces leakage prevention, deterministic replay, multi-seed robustness, and resource budgets before any candidate is admitted. Across eight anonymized evolutionary operators, the best system reaches a Combined Score of 0.9335 with sign-accuracy 0.9180, while trajectory and ablation analyses show that adaptive sampling and island migration are primary drivers of convergence quality. The deployment model is explicitly ``screen-and-escalate'': surrogates provide high-throughput ranking for design exploration, but low-confidence or out-of-distribution cases are automatically escalated to high-fidelity CFD. The resulting contribution is an auditable, reusable workflow for accelerating aerodynamic design iteration while preserving decision-grade reliability, governance traceability, and safety boundaries.