GitHub topics: adjoint
SciML/SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
Language: Julia - Size: 86 MB - Last synced at: about 22 hours ago - Pushed at: about 22 hours ago - Stars: 348 - Forks: 73
mdolab/dafoam
DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization
Language: C - Size: 10.6 MB - Last synced at: 1 day ago - Pushed at: 1 day ago - Stars: 260 - Forks: 94
tlm-adjoint/tlm_adjoint
A library for high-level algorithmic differentiation
Language: Python - Size: 4.8 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 18 - Forks: 7
SciML/SciMLWorkshop.jl
Workshop materials for training in scientific computing and scientific machine learning
Language: Julia - Size: 62.2 MB - Last synced at: 5 days ago - Pushed at: about 1 year ago - Stars: 38 - Forks: 5
google/ceviche-challenges 📦
A suite of photonic inverse design challenge problems for topology optimization benchmarking
Language: Python - Size: 743 KB - Last synced at: 5 days ago - Pushed at: over 1 year ago - Stars: 106 - Forks: 12
gaelforget/MITgcm.jl
Julia interface to MITgcm
Language: Julia - Size: 66.5 MB - Last synced at: 5 days ago - Pushed at: 14 days ago - Stars: 34 - Forks: 9
fancompute/ceviche
:shrimp: Electromagnetic Simulation + Automatic Differentiation
Language: Python - Size: 16 MB - Last synced at: 5 days ago - Pushed at: almost 2 years ago - Stars: 358 - Forks: 79
fancompute/angler
Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices
Language: Python - Size: 117 MB - Last synced at: 5 days ago - Pushed at: over 5 years ago - Stars: 161 - Forks: 50
IvanYashchuk/jax-fenics
Differentiable interface to FEniCS for JAX
Language: Python - Size: 67.4 KB - Last synced at: about 1 month ago - Pushed at: almost 4 years ago - Stars: 53 - Forks: 5
IvanYashchuk/jax-fenics-adjoint
Differentiable interface to FEniCS/Firedrake for JAX using dolfin-adjoint/pyadjoint
Language: Jupyter Notebook - Size: 110 KB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 93 - Forks: 12
twhughes/PhD_Thesis
Adjoint-based optimization and inverse design of photonic devices.
Language: TeX - Size: 30 MB - Last synced at: 2 months ago - Pushed at: over 5 years ago - Stars: 12 - Forks: 7
ComputationalPhysiology/pulse_adjoint
An adjointable cardiac mechanics data assimilator.
Language: Python - Size: 16.9 MB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0
ocramz/ad-delcont
Reverse-mode automatic differentiation with delimited continuations
Language: Haskell - Size: 46.9 KB - Last synced at: 29 days ago - Pushed at: almost 2 years ago - Stars: 15 - Forks: 2
IvanYashchuk/PyFenicsAD.jl
Automatic differentiation of FEniCS and Firedrake models in Julia
Language: Julia - Size: 27.3 KB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 13 - Forks: 1
pyroteus/pyroteus 📦
Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake
Language: Python - Size: 5.93 MB - Last synced at: 5 months ago - Pushed at: over 1 year ago - Stars: 11 - Forks: 3
CFD-GO/TCLB
TCLB - Templated MPI+CUDA/CPU Lattice Boltzmann code
Language: C++ - Size: 21.7 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 171 - Forks: 70
DaniJonesOcean/AdjointAnalysis
Create animations, plots, and calculate summary statistics for MITgcm adjoint output
Language: MATLAB - Size: 111 KB - Last synced at: 11 months ago - Pushed at: 12 months ago - Stars: 2 - Forks: 2
IvanYashchuk/jax-firedrake
Differentiable interface to Firedrake for JAX
Size: 3.91 KB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 14 - Forks: 0
brmather/conduction
Python package for solving implicit heat conduction
Language: Jupyter Notebook - Size: 3.74 MB - Last synced at: 5 days ago - Pushed at: about 4 years ago - Stars: 8 - Forks: 4
masyuraC7/invers-matriks-kofaktor-proyek-kuliah
Aplikasi berbasis web untuk memenuhi Tugas Akhir Mata Kuliah Aljabar Linier & Matriks. Aplikasi ini dapat menghitung kofaktor matriks menggunakan cara Kofaktor(Adjoint)
Language: PHP - Size: 418 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0
drgHannah/Radon-Transformation
A Pytorch implementation of the radon operator and filtered backprojection with, except for a constant, adjoint radon operator and backprojection.
Language: Jupyter Notebook - Size: 667 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 14 - Forks: 0
IvanYashchuk/numpy-fenics-adjoint 📦
Easy interoperability with Automatic Differentiation libraries through NumPy interface to FEniCS adjoint
Language: Python - Size: 19.5 KB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 0
IvanYashchuk/fenics-pymc3 📦
Differentiable interface to FEniCS for PyMC3
Language: Python - Size: 60.5 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 18 - Forks: 3
comp-physics/1D-Shocks-Adjoint
A shock-capturing adjoint solver for the compressible flow equations
Language: Fortran - Size: 70.3 KB - Last synced at: 7 days ago - Pushed at: about 5 years ago - Stars: 4 - Forks: 1
choward1491/AdjointOptimalControl
Approximation algorithm to solve Optimal Control problems using the Adjoint Method. Assumes your controller is based on a parametric model. Uses Forward-Backward-Sweep adjoint method.
Language: C++ - Size: 4.93 MB - Last synced at: about 2 years ago - Pushed at: over 7 years ago - Stars: 4 - Forks: 0
jholland1/py_1D_heat
1D Heat Equation Model Problem for Field Inversion and Machine Learning Demonstration
Language: Python - Size: 38.1 KB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 2
bgranzow/goal
Goal Oriented Adaptive Lagrangian Mechanics
Language: C++ - Size: 669 KB - Last synced at: 11 days ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 2
ambrad/kfgs
Compute the gradient of the log likelihood function from a Kalman filter using the adjoint method.
Language: MATLAB - Size: 330 KB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 2
kubawajs/AdjointGraph-Converter
Combinatorial algorithms in bioinformatics - Adjoint Graph
Language: C++ - Size: 278 KB - Last synced at: almost 2 years ago - Pushed at: over 8 years ago - Stars: 0 - Forks: 0
