Scientific Machine Learning • Graph AI • Foundation Models
This repository hosts the research poster for the GIFT-KASTL project, exploring the use of graph foundation models for fracture network learning in scientific machine learning.
Fracture systems arise in many scientific and engineering domains including:
• subsurface modeling
• geophysics
• material science
• fluid flow systems
Modeling these systems presents unique challenges due to their complex topology, high dimensional geometry, and large-scale simulation costs.
The GIFT-KASTL framework investigates how graph-based machine learning architectures can capture the structure of fracture networks and enable data-driven surrogate modeling of physical systems.
Traditional physics-based simulation methods for fracture networks often require expensive numerical solvers.
Machine learning offers an alternative paradigm:
learning representations directly from structured data.
Graph-based representations are particularly well-suited for fracture systems because they preserve:
• connectivity
• topology
• spatial relationships
Recent advances in graph neural networks and foundation models suggest new possibilities for scalable scientific learning.
The GIFT-KASTL framework investigates a pipeline consisting of:
Fracture systems are encoded as graphs where
• nodes represent fracture elements
• edges represent interactions and connectivity
This representation naturally captures structural relationships.
The framework explores graph-based neural architectures capable of modeling complex interactions in fracture systems.
Key elements include:
• graph representation learning
• scalable neural architectures
• structured scientific datasets
The system aims to support:
• surrogate modeling
• predictive simulation
• data-driven physical modeling
Potential application areas include
• fracture propagation prediction
• subsurface simulation
• material failure modeling
• accelerated scientific simulation
Himanshu Singh
Research interests include:
• scientific machine learning
• graph foundation models
• sparse learning
• mechanistic interpretability
You may also find these related repositories interesting:
• Mechanistic AI Interpretability
• Sparse Neural Networks
• Fourier Neural Operator Learning
Explore more at:
https://github.com/himanshuvnm
If you use or reference this work, please cite the repository:
GitHub
https://github.com/himanshuvnm
LinkedIn
https://linkedin.com/in/himanshuvnm