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

2026-03-23

AI Summary

This work addresses the key bottleneck in high-fidelity aerodynamic drag prediction for vehicles—namely, geometric preprocessing, mesh generation, resource contention, and reproducibility challenges—rather than solver runtime. The authors propose a contract-centered, self-evolving coded agent framework that formulates drag coefficient prediction as a constrained optimization problem in program space. By leveraging multi-objective selection and structured mutation operators spanning data, models, loss functions, and partitioning strategies, the framework generates executable and auditable surrogate pipelines. A key innovation lies in evolving complete, executable programs while enforcing hard evaluation contracts to guarantee leak-free, replayable, and robust evaluations. A “filter-and-upgrade” deployment strategy balances efficiency and reliability. The best-performing system achieves a Combined Score of 0.9335 and a symbolic accuracy of 0.9180, demonstrating the critical role of adaptive sampling and island migration in enhancing convergence quality.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• vehicle aerodynamic drag
• workflow friction
• surrogate modeling
• reproducibility
• high-fidelity CFD

Innovation

• self-evolving agents
• surrogate discovery
• constrained program optimization
• multi-objective evolution
• auditable AI workflow