Quercus Hernández · Emmi AI
Recorded at the CDFAM Computational Design Symposium, Barcelona 2026.
In this episode
Presentation Abstract
Engineering is entering a new paradigm where AI is no longer limited to accelerating isolated simulations, but is becoming a foundational layer of the industrial design and manufacturing stack. Across sectors such as automotive, energy, semiconductors, and aerospace, engineering workflows remain constrained by slow simulation feedback loops, fragmented CAD-to-CAE pipelines, and computational bottlenecks that limit design exploration and innovation speed.
In this talk, we present the vision of Large Engineering Models developed at Emmi AI: physics-aware foundation models designed to operate directly on industrial geometry and process inputs while replacing large parts of the traditional numerical simulation workflow. Rather than focusing on narrow surrogate models, this approach consolidates geometry processing, physics prediction, and post-processing into a unified AI-native engineering interface that provides near real-time feedback to engineers and designers.
As a concrete industry showcase, we present NeuralMould, our first commercially deployed Large Engineering Model for injection moulding. The model enables engineers to evaluate design changes such as gate placement, material selection, and process parameters with near-instant feedback, eliminating traditional meshing bottlenecks and dramatically accelerating design iteration cycles. In addition, we introduce Noether, our recently released open-source framework for building and deploying engineering AI models at scale. Noether provides the infrastructure for training, fine-tuning, and operating Large Engineering Models across domains, enabling industrial partners and researchers to develop their own AI-native simulation capabilities.
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