jdtoscano94/NABLA-SciML
Physics-Informed Neural Networks and deep learning frameworks for scientific computing applications like fluid dynamics and brain fluid flow.
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NABLA-SciML is a unified framework for efficient and reproducible implementations of scientific machine learning methods including Physics-Informed Neural Networks (PINNs), DeepONets, and Kolmogorov-Arnold Networks (KANs). The repository contains tutorial modules demonstrating these architectures using both PyTorch and JAX, alongside research modules for Residual-Based Attention mechanisms, variational frameworks, and applications to complex physical systems such as turbulent flows and cerebrospinal fluid dynamics.
Frequently asked
- What is jdtoscano94/NABLA-SciML?
- Physics-Informed Neural Networks and deep learning frameworks for scientific computing applications like fluid dynamics and brain fluid flow.
- Is NABLA-SciML open source?
- Yes — jdtoscano94/NABLA-SciML is an open-source project tracked on heatdrop.
- What language is NABLA-SciML written in?
- jdtoscano94/NABLA-SciML is primarily written in Jupyter Notebook.
- How popular is NABLA-SciML?
- jdtoscano94/NABLA-SciML has 677 stars on GitHub.
- Where can I find NABLA-SciML?
- jdtoscano94/NABLA-SciML is on GitHub at https://github.com/jdtoscano94/NABLA-SciML.