A Creator-Inspector multi-agent LLM pipeline for constitutive artificial neural networks increases the rate of models satisfying all nine physical constraints to 100% or 56% depending on the LLM backbone.
arXiv preprint arXiv:2512.01735 , year=
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GPT-Micro autonomously discovers thermodynamics-compliant constitutive models from literature and sparse data via LLM hypothesis generation and refinement, claiming 70% less data and 400X faster discovery than prior approaches on a printed-electronics test case.
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
citing papers explorer
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LLM-driven design of physics-constrained constitutive models: two agents are better than one
A Creator-Inspector multi-agent LLM pipeline for constitutive artificial neural networks increases the rate of models satisfying all nine physical constraints to 100% or 56% depending on the LLM backbone.
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GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
GPT-Micro autonomously discovers thermodynamics-compliant constitutive models from literature and sparse data via LLM hypothesis generation and refinement, claiming 70% less data and 400X faster discovery than prior approaches on a printed-electronics test case.
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LEIA: Learned Environment for Interactive Architected Materials
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.