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Researchers from MIT and others developed GIFT, a system that teaches vision-language models to automatically convert 2D designs into accurate CAD programs for rapid prototyping, using model-generated data to correct mistakes and improve performance with less computation.
This paper proposes automation-in-design (AiD) using computational design synthesis with deep learning and generative AI to automate the creation of novel high-tech systems, demonstrated in two case studies (e-drive and spatial dimensioning).
BIM-Edit is a benchmark for evaluating LLMs on natural-language editing of Building Information Models (BIM) in IFC format. Results show a substantial gap, with the best model achieving only 49.5% average score across geometric, semantic, and topological metrics.
This paper proposes eCNNTO, a CNN with residual connections to accelerate density-based topology optimization by predicting near-optimal densities from early iteration histories, achieving up to 97% reduction in iterations and strong generalization across different boundary conditions, geometries, and mesh resolutions.
EngiAI introduces a multi-agent framework and benchmark suite for LLM-driven engineering design, evaluating workflow, RAG, and HPC dimensions. Proprietary models achieve 96-97% task completion on Beams2D, while conditional branching remains challenging with 20-53% for Photonics2D.