Adaptive Refinments callbacks

class R3Refinement(sample_every)

Bases: Callback

PINA Implementation of an R3 Refinement Callback.

This callback implements the R3 (Retain-Resample-Release) routine for sampling new points based on adaptive search. The algorithm incrementally accumulates collocation points in regions of high PDE residuals, and releases those with low residuals. Points are sampled uniformly in all regions where sampling is needed.

See also

Original Reference: Daw, Arka, et al. Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. (2023). DOI: 10.48550/arXiv.2207.02338

Parameters:

sample_every (int) – Frequency for sampling.

Raises:

ValueError – If sample_every is not an integer.

Example

>>> r3_callback = R3Refinement(sample_every=5)

on_train_start(trainer, _)

Callback function called at the start of training.

This method extracts the locations for sampling from the problem conditions and calculates the total population.

Parameters:

• trainer (pytorch_lightning.Trainer) – The trainer object managing the training process.

• _ – Placeholder argument (not used).

Returns:

None

Return type:

None

on_train_epoch_end(trainer, __)

Callback function called at the end of each training epoch.

This method triggers the R3 routine for refinement if the current epoch is a multiple of _sample_every.

Parameters:

• trainer (pytorch_lightning.Trainer) – The trainer object managing the training process.

• __ – Placeholder argument (not used).

Returns:

None

Return type:

None