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Found 9 papers, 1 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.
A Thermal Machine Learning Solver For Chip Simulation
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration.
A composable autoencoder-based iterative algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
A composable autoencoder-based algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
Geometry encoding for numerical simulations
We present a notion of geometry encoding suitable for machine learning-based numerical simulation.
A Latent space solver for PDE generalization
In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space.
One-shot learning for solution operators of partial differential equations
Discovering governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering.
Active Deep Learning on Entity Resolution by Risk Sampling
Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER.
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient
Specifically, a hybrid framework of Auto Encoder (AE) and Image Gradient (IG) based network is designed.
