The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning. Learn more →
Julia scientific-ai Projects
-
NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
-
DiffEqFlux.jl
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
The documentation has a manifest associated with it: https://docs.sciml.ai/NeuralPDE/dev/#Reproducibility. Instantiating the manifest will give you all of the exact versions used for the documentation build (https://github.com/SciML/NeuralPDE.jl/blob/gh-pages/v5.7.0/assets/Manifest.toml). You just ]instantiate folder_of_manifest. Or you can use the Project.toml.
Julia scientific-ai related posts
- Automatically install huge number of dependency?
- from Wolfram Mathematica to Julia
- [D] ICLR 2022 RESULTS ARE OUT
- [N] Open Colloquium by Prof. Max Welling: "Is the next deep learning disruption in the physical sciences?"
- [D] What are some ideas that are hyped up in machine learning research but don't actually get used in industry (and vice versa)?
-
A note from our sponsor - WorkOS
workos.com | 28 Apr 2024
Index
Project | Stars | |
---|---|---|
1 | NeuralPDE.jl | 903 |
2 | DiffEqFlux.jl | 837 |
Sponsored