pdPINN

Files

The code for the individual experiments is provided in the following directories: Each experiment uses a different conda environment, which is provided in form of '.yml' files in the respective directories.

Mass Conservation Experiments

Setup

The environment information is given in env.yml. We propose to the package manager mamba for creating the environment with: mamba env create -f env.yml

Alternatively, conda should also work (by simply replacing mamba with conda), but it was only tested using mamba.

Experiments

Via command-line arguments the settings of the training procedure can be set for each experiment. E.g.: python experiment_3d.py --n-samples 2048 --sampling-method mh_pdpinn --pde-weight 400

Note, that --sampling-method mh_pdpinn refers to the proposed pdPINN, --sampling-method uniform to the uniform sampler. Adaptive Refinement can be included via the flags --rar and --ot-rar.

Data

Implementation

Videos

Here we can see a comparison between MH-pdPINN and OT-RAR for 2000 collocation points in the 3D experiment. The noteworthy difference is the vanishing and reappearing of density, which is more pronounced in the first two columns.

More videos can be found in the Videos/ directory.

Experiment 3D Baseline Experiment 3D, OT-RAR, ~2000 samples Experiment 3D, MH-pdPINN, ~2000 samples Groundtruth