Julia bindings for the Enzyme automatic differentiator
-
Updated
Jun 11, 2024 - Julia
Julia bindings for the Enzyme automatic differentiator
High-performance automatic differentiation of LLVM and MLIR.
PotentialLearning.jl: Developing Optimization Workflows for Fast and Accurate Interatomic Potentials.
Productive, portable, and performant GPU programming in Python.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Probabilistic Answer Set Programming and Probabilistic SAT solving, based on Differentiable Satisfiability
A differentiable bridge between phase space and Fock space
A 6D differentiable underwater vehicle dynamics in body, ned and quaternion coordinate.
👀🔥 Differentiable foveated vision for Deep Learning methods
Robot kinematics implemented in pytorch
Compositional Differentiable Programming Library
Deep relational learning through differentiable logic programming.
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
End-to-end differentiable blind tip reconstruction on Colab implemented with PyTorch
Differentiable Symbolic Specification
learning state-space targets in dynamical systems
Differentiating convex optimization programs w.r.t. program parameters
Add a description, image, and links to the differentiable-programming topic page so that developers can more easily learn about it.
To associate your repository with the differentiable-programming topic, visit your repo's landing page and select "manage topics."