IEEE

Proper Orthogonal Decomposition for Model Order Reduction of High Dimensional Parametric Problems with Varying Geometries Based on the Interface Adaptive Mesh

Abstract: The proper orthogonal decomposition (POD) is commonly applied for the model order reduction (MOR) of parametric problems. The method generates a reduced basis from the left singular vectors ...
Journal of Medical Internet Research

How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective

Moreover, we discuss strategies for metadata selection and human evaluation to ensure the quality and effectiveness of ITDs. By integrating these elements, this tutorial provides a structured ...
GitHub

terng03412/yolo_model_deployment_tutorial

yolo-fastapi-deployment/ ├── app/ │ ├── __init__.py │ ├── main.py │ ├── models/ │ │ ├── __init__.py │ │ └── yolo ...
IEEE

CAD-Llama: Leveraging Large Language Models for Computer-Aided Design Parametric 3D Model Generation

Abstract: Recently, Large Language Models (LLMs) have achieved significant success, prompting increased interest in expanding their generative capabilities beyond general text into domain-specific ...
GitHub

A PyTorch implementation of Parametric UMAP (Uniform Manifold Approximation and Projection) for learning low-dimensional parametric embeddings of high-dimensional data.

The pip install pulls the default PyTorch build from PyPI. If you need a specific CUDA version, install PyTorch first following the official instructions, then install this package.