{"slug": "surrogate-assisted-pedestrian-protection-design-via-a-foundation-model-workflow", "title": "Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow", "summary": "Researchers introduced the first foundation model-orchestrated workflow for crash safety design, enabling surrogate-assisted pedestrian protection exploration that reduces evaluation time from hours to seconds. The workflow integrates a surrogate model, multiobjective evolutionary search, a morphing-based geometry generator, and a natural-language interface, producing 35 safety-compliant alternatives in a single exploration. This approach demonstrates that foundation models can serve as integration layers between ML surrogates and physics-based simulation in safety-critical engineering.", "body_md": "arXiv:2606.17577v1 Announce Type: new\nAbstract: AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models. To the best of our knowledge, we present the first foundation model--orchestrated workflow for crash safety design that enables surrogate-assisted exploration for pedestrian protection, reducing evaluation time from hours per CAE simulation to seconds.\nThe workflow integrates four components: (1) a surrogate trained on CAE crash simulations to predict pedestrian leg injury metrics from design parameters, achieving an average $R^2=0.87$ and providing distribution-free conformal prediction intervals; (2) multiobjective evolutionary search (NSGA-II) to discover diverse feasible parameter sets under user-specified constraints; (3) a morphing-based geometry generator that maps parameters to topology-preserving 3D shapes; and (4) a natural-language interface in which an LLM orchestrates the workflow and a vision--language model supports semantic comparison of generated designs.\nIn an automotive front-bumper case study, the workflow produces 35 distinct safety-compliant alternatives from a single exploration, a process that would require weeks with conventional CAE iteration. These results suggest that foundation models can serve as integration layers between ML surrogates and physics-based simulation, helping bring AI capabilities to safety-critical engineering domains.", "url": "https://wpnews.pro/news/surrogate-assisted-pedestrian-protection-design-via-a-foundation-model-workflow", "canonical_source": "https://arxiv.org/abs/2606.17577", "published_at": "2026-06-17 04:00:00+00:00", "updated_at": "2026-06-17 04:23:57.266355+00:00", "lang": "en", "topics": ["machine-learning", "large-language-models", "ai-research", "ai-tools", "computer-vision"], "entities": ["arXiv", "NSGA-II"], "alternates": {"html": "https://wpnews.pro/news/surrogate-assisted-pedestrian-protection-design-via-a-foundation-model-workflow", "markdown": "https://wpnews.pro/news/surrogate-assisted-pedestrian-protection-design-via-a-foundation-model-workflow.md", "text": "https://wpnews.pro/news/surrogate-assisted-pedestrian-protection-design-via-a-foundation-model-workflow.txt", "jsonld": "https://wpnews.pro/news/surrogate-assisted-pedestrian-protection-design-via-a-foundation-model-workflow.jsonld"}}