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Found 3 papers, 0 papers with code
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model
We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data renovation techniques to improve the semantic representation of embeddings' space; (ii) introducing the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data renovation reliability; (iii) developing the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique; and (iv) effectively refactoring existing scripts to generate new and high-quality scripts with LLMs.
Diffusion model based data generation for partial differential equations
In a preliminary attempt to address the problem of data scarcity in physics-based machine learning, we introduce a novel methodology for data generation in physics-based simulations.
Systematic Analysis of Image Generation using GANs
The advantages of GANs for image generation over conventional methods as well their disadvantages amongst other frameworks are presented.
