GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
arXiv:2606.08238v1 Announce Type: new Abstract: Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material microstructure in manufacturing processes. The limited accuracy resulting from the typical reliance on fallible human expertise and intuition for postulation and revision of the models functional form results in incremental and time consuming model discovery. Conventional Machine Learning (ML) incurs significant cost and time of data generation. Model discovery using Large Language Models (LLMs)
The rapid advancement of Large Language Models (LLMs) is enabling their application to complex scientific and engineering problems previously constrained by traditional methods.
This development represents a significant step towards automating fundamental material science, potentially accelerating innovation and efficiency within critical manufacturing sectors.
Material discovery processes can move beyond human intuition and cost-prohibitive traditional ML approaches, becoming faster, more consistent, and less expensive.
- · Manufacturing sector
- · Materials science researchers
- · AI model developers
- · Semiconductor industry
- · Traditional material discovery consultancies
- · Companies reliant on slow R&D cycles
Accelerated discovery of new materials with superior properties for various applications.
Increased efficiency and reduced costs in manufacturing processes across multiple industries due to optimized material selection.
Potential for new product categories and market disruptions driven by previously unattainable material capabilities.
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Read at arXiv cs.LG