文件列表:

ABM/
NCA/
PDE/
jax_eddie_test.py
jax_eddie_test.sh
micropattern_radii.array.sh
micropattern_radii.sh
micropattern_radii_evaluate.sh
micropattern_radii_random.py
micropattern_radii_sizes.py
micropattern_radii_sizes_test.py
test_jax.py
test_jax_random.py
test_micropattern.py
test_pde.py
test_pde_gpu.py
texture_nca.py

# Differentiable-Patterning A collection of different projects and ideas that use differentiable programming to explore self organising systems with emergent pattern formation. Primarily for my PhD research ## Requirements - tensorflow 2.13.0 (just for tensorboard logging) - tensorboard 2.13.0 - numpy 1.24.4 - scipy 1.9.0 - scikit-image 0.19.1 - tqdm 4.64.0 - matplotlib 3.7.2 - jax 0.4.13 - jaxlib 0.4.13 - optax 0.1.7 - equinox 0.10.4 ## Code Structure - Neural Cellular Automata (NCA) - NCA/model/NCA_model.py includes a JAX/Equinox implementation of the NCA model - NCA/model/boundary.py includes handling of complex boundary conditions and hard coded environment channels - NCA/trainer/NCA_trainer.py includes a class that uses Optax to fit the NCA models to data - NCA/trainer/data_augmenter.py includes a class for augmenting data during and before training. Handles explicit multi-gpu parallelism as well. - NCA/trainer/data_augmenter_tree.py performs the same data augmentation as above, but on PyTrees of data, to allow training simultaneously on multiple trajectories of different sizes - NCA/trainer/loss.py includes loss function definitions - NCA/trainer/tensorboard_log.py logs performance of model during training to tensorboard - NCA/NCA_visualiser.py includes methods for visualising and interpretting trained NCA - NCA/utils.py includes various file io and data pre-processing methods - Partial Differential Equations (PDE) - PDE/reaction_diffusion_advection/update.py includes a spatially discretised version of a general multi-species reaction diffusion advection equation, as an Equinox module - PDE/solver/semi_discrete_solver.py includes an auto-differentiable numerical solver of spatially discretised PDEs, implemented in Diffrax - PDE/trainer/data_augmenter_pde.py includes a subclass of NCA/trainer/data_augmenter_tree, tailored to PDE training - PDE/trainer/PDE_trainer.py includes a class that uses Optax to fit PDE paramaters such that the solutions of the PDE approximate a given time series - PDE/trainer/optimiser.py includes a custom Optax optimiser that keeps diffusion coefficients non-negative - PDE/trainer/tensorboard_log.py logs performance of model during training to tensorboard - PDE/PDE_visualiser.py includes methods for visualising and interpretting trained PDE

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