CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[2.0.0a0] - 2026-XX-YY

Added

• Refactored diffusion preconditioners in physicsnemo.diffusion.preconditioners relying on a new abstract base class BaseAffinePreconditioner for preconditioning schemes using affine transformations. Existing preconditioners (VPPrecond, VEPrecond, iDDPMPrecond, EDMPrecond) reimplemented based on this new interface.
• New SO2Convolution layer in physicsnemo.experimental.nn.symmetry using grid-based layout for efficient GPU parallelization. The new implementation uses a single vectorized einsum operation instead of per-m-order loops.

Changed

• PhysicsNemo v2.0 contains significant reorganization of tools. Please see the v2.0-MIGRATION-GUIDE.md to understand what has changed and why.

Deprecated

Removed

Fixed

Security

Dependencies

[1.3.0] - 2025-11-17

Added

• Added mixture_of_experts for weather example in physicsnemo.examples.weather. ⚠️Warning: - It uses experimental DiT model subject to future API changes. Added some modifications to DiT architecture in physicsnemo.experimental.models.dit. Added learnable option to PositionalEmbedding in physicsnemo.models.diffusion.layers.
• Added lead-time aware training support to the StormCast example.
• Add a device aware kNN method to physicsnemo.utils.neighbors. Works with CPU or GPU by dispatching to the proper optimized library, and torch.compile compatible.
• Added additional testing of the DoMINO datapipe.
• Examples: added a new example for full-waveform inversion using diffusion models. Accessible in examples/geophysics/diffusion_fwi.
• Domain Parallelism: Domain Parallelism is now available for kNN, radius_search, and torch.nn.functional.pad.
• Unified recipe for crash modeling, supporting Transolver and MeshGraphNet, and three transient schemes.
• Added a check to stochastic_sampler that helps handle the EDMPrecond model, which has a specific .forward() signature
• Examples: added a new example for reservoir simulation using X-MeshGraphNet. Accessible in examples/reservoir_simulation
• Added abstract interfaces for constructing active learning workflows, contained under the physicsnemo.active_learning namespace. A preliminary example of how to compose and define an active learning workflow is provided in examples/active_learning. The moons example provides a minimal (pedagogical) composition that is meant to illustrate how to define the necessary parts of the workflow.
• Added a new example for temporal interpolation of weather forecasts using ModAFNO. Accessible in examples/weather/temporal_interpolation.

Changed

• Migrated Stokes MGN example to PyTorch Geometric.
• Migrated Lennard Jones example to PyTorch Geometric.
• Migrated physicsnemo.utils.sdf.signed_distance_field to a static return, torch-only interface. It also now works on distributed meshes and input fields.
• Refactored DiTBlock to be more modular
• Added NATTEN 2D neighborhood attention backend for DiTBlock
• Migrated blood flow example to PyTorch Geometric.
• Refactored DoMINO model code and examples for performance optimizations and improved readability.
• Migrated HydroGraphNet example to PyTorch Geometric.
• Support for saving and loading nested physicsnemo.Modules. It is now possible to create nested modules with m = Module(submodule, ...), and save and load them with Module.save and Module.from_checkpoint. ⚠️Warning: - The modules have to be physicsnemo.Modules, and not torch.nn.Modules.
• Support passing custom tokenizer, detokenizer, and attention Modules in experimental DiT architecture
• Improved Transolver training recipe's configuration for checkpointing and normalization.
• Bumped multi-storage-client version to 0.33.0 with rust client.
• Improved configuration for DLWP Healpix (checkpoint directory) and GraphCast (W&B settings).

Fixed

• Set skip_scale to Python float in U-Net to ensure compilation works.
• Ensure stream dependencies are handled correctly in physicsnemo.utils.neighbors
• Fixed the issue with incorrect handling of files with consecutive runs of combine_stl_solids.py in the X-MGN recipe.
• Fixed the RuntimeError: Worker data receiving interrupted error in the datacenter example.

[1.2.0] - 2025-08-26

Added

• Diffusion Transformer (DiT) model. The DiT model can be accessed in physicsnemo.experimental.models.dit.DiT. ⚠️Warning: - Experimental feature subject to future API changes.
• Improved documentation for diffusion models and diffusion utils.
• Safe API to override __init__'s arguments saved in checkpoint file with Module.from_checkpoint("chkpt.mdlus", override_args=set(...)).
• PyTorch Geometric MeshGraphNet backend.
• Functionality in DoMINO to take arbitrary number of scalar or vector global parameters and encode them using class ParameterModel
• TopoDiff model and example.
• Added ability for DoMINO model to return volume neighbors.
• Added functionality in DoMINO recipe to introduce physics residual losses.
• Diffusion models, metrics, and utils: implementation of Student-t distribution for EDM-based diffusion models (t-EDM). This feature is adapted from the paper Heavy-Tailed Diffusion Models, Pandey et al.. This includes a new EDM preconditioner (tEDMPrecondSuperRes), a loss function (tEDMResidualLoss), and a new option in corrdiff diffusion_step. ⚠️ This is an experimental feature that can be accessed through the physicsnemo.experimental module; it might also be subjected to API changes without notice.
• Bumped Ruff version from 0.0.290 to 0.12.5. Replaced Black with ruff-format.
• Domino improvements with Unet attention module and user configs
• Hybrid MeshGraphNet for modeling structural deformation
• Enabled TransformerEngine backend in the transolver model.
• Inference code for x-meshgraphnet example for external aerodynamics.
• Added a new example for external_aerodynamics: training transolver on irregular mesh data for DrivaerML surface data.
• Added a new example for external aerodynamics for finetuning pretrained models.

Changed

• Diffusion utils: physicsnemo.utils.generative renamed into physicsnemo.utils.diffusion
• Diffusion models: in CorrDiff model wrappers (EDMPrecondSuperResolution and UNet), the arguments profile_mode and amp_mode cannot be overriden by from_checkpoint. They are now properties that can be dynamically changed after the model instantiation with, for example, model.amp_mode = True and model.profile_mode = False.
• Updated healpix data module to use correct DistributedSampler target for test data loader
• Existing DGL-based vortex shedding example has been renamed to vortex_shedding_mgn_dgl. Added new vortex_shedding_mgn example that uses PyTorch Geometric instead.
• HEALPixLayer can now use earth2grid HEALPix padding ops, if desired
• Migrated Vortex Shedding Reduced Mesh example to PyTorch Geometric.
• CorrDiff example: fixed bugs when training regression UNet.
• Diffusion models: fixed bugs related to gradient checkpointing on non-square images.
• Diffusion models: created a separate class Attention for clarity and modularity. Updated UNetBlock accordingly to use the Attention class instead of custom attention logic. This will update the model architecture for SongUNet-based diffusion models. Changes are not BC-breaking and are transparent to the user.
• ⚠️ BC-breaking: refactored the automatic mixed precision (AMP) API in layers and models defined in physicsnemo/models/diffusion/ for improved usability. Note: it is now, not only possible, but required to explicitly set model.amp_mode = True in order to use the model in a torch.autocast clause. This applies to all SongUNet-based models.
• Diffusion models: fixed and improved API to enable fp16 forward pass in UNet and EDMPrecondSuperResolution model wrappers; fp16 forward pass can now be toggled/untoggled by setting model.use_fp16 = True.
• Diffusion models: improved API for Apex group norm. SongUNet-based models will automatically perform conversion of the input tensors to torch.channels_last memory format when model.use_apex_gn is True. New warnings are raised when attempting to use Apex group norm on CPU.
• Diffusion utils: systematic compilation of patching operations in stochastic_sampler for improved performance.
• CorrDiff example: added option for Student-t EDM (t-EDM) in train.py and generate.py. When training a CorrDiff diffusion model, this feature can be enabled with the hydra overrides ++training.hp.distribution=student_t and ++training.hp.nu_student_t=<nu_value>. For generation, this feature can be enabled with similar overrides: ++generation.distribution=student_t and ++generation.nu_student_t=<nu_value>.
• CorrDiff example: the parameters P_mean and P_std (used to compute the noise level sigma) are now configurable. They can be set with the hydra overrides ++training.hp.P_mean=<P_mean_value> and ++training.hp.P_std=<P_std_value> for training (and similar ones with training.hp replaced by generation for generation).
• Diffusion utils: patch-based inference and lead time support with deterministic sampler.
• Existing DGL-based XAeroNet example has been renamed to xaeronet_dgl. Added new xaeronet example that uses PyTorch Geometric instead.
• Updated the deforming plate example to use the Hybrid MeshGraphNet model.
• ⚠️ BC-breaking: Refactored the transolver model to improve readability and performance, and extend to more use cases.
• Diffusion models: improved lead time support for SongUNetPosLtEmbd and EDMLoss. Lead-time embeddings can now be used with/without positional embeddings.
• Diffusion models: consolidate ApexGroupNorm and GroupNorm in models/diffusion/layers.py with a factory get_group_norm that can be used to instantiate either one of them. get_group_norm is now the recommended way to instantiate a GroupNorm layer in SongUNet-based and other diffusion models.
• Physicsnemo models: improved checkpoint loading API in Module.from_checkpoint that now exposes a strict parameter to raise error on missing/unexpected keys, similar to that used in torch.nn.Module.load_state_dict.
• Migrated Hybrid MGN and deforming plate example to PyTorch Geometric.

Fixed

• Bug fixes in DoMINO model in sphere sampling and tensor reshaping
• Bug fixes in DoMINO utils random sampling and test.py
• Optimized DoMINO config params based on DrivAer ML

[1.1.1] - 2025-06-16

Fixed

• Fixed an inadvertent change to the deterministic sampler 2nd order correction
• Bug Fix in Domino model ball query layer
• Fixed bug models/unet/unet.py: setting num_conv_layers=1 gives errors

[1.1.0] - 2025-06-05

Added

• Added ReGen score-based data assimilation example
• General purpose patching API for patch-based diffusion
• New positional embedding selection strategy for CorrDiff SongUNet models
• Added Multi-Storage Client to allow checkpointing to/from Object Storage
• Added a new aerodynamics example using DoMINO to compute design sensitivities (e.g., drag adjoint) with respect to underlying input geometry.

Changed

• Simplified CorrDiff config files, updated default values
• Refactored CorrDiff losses and samplers to use the patching API
• Support for non-square images and patches in patch-based diffusion
• ERA5 download example updated to use current file format convention and restricts global statistics computation to the training set
• Support for training custom StormCast models and various other improvements for StormCast
• Updated CorrDiff training code to support multiple patch iterations to amortize regression cost and usage of torch.compile
• Refactored physicsnemo/models/diffusion/layers.py to optimize data type casting workflow, avoiding unnecessary casting under autocast mode
• Refactored Conv2d to enable fusion of conv2d with bias addition
• Refactored GroupNorm, UNetBlock, SongUNet, SongUNetPosEmbd to support usage of Apex GroupNorm, fusion of activation with GroupNorm, and AMP workflow.
• Updated SongUNetPosEmbd to avoid unnecessary HtoD Memcpy of pos_embd
• Updated from_checkpoint to accommodate conversion between Apex optimized ckp and non-optimized ckp
• Refactored CorrDiff NVTX annotation workflow to be configurable
• Refactored ResidualLoss to support patch-accumlating training for amortizing regression costs
• Explicit handling of Warp device for ball query and sdf
• Merged SongUNetPosLtEmb with SongUNetPosEmb, add support for batch>1
• Add lead time embedding support for positional_embedding_selector. Enable arbitrary positioning of probabilistic variables
• Enable lead time aware regression without CE loss
• Bumped minimum PyTorch version from 2.0.0 to 2.4.0, to minimize support surface for physicsnemo.distributed functionality.

Dependencies

• Made nvidia.dali an optional dependency

[1.0.1] - 2025-03-25

Added

• Added version checks to ensure compatibility with older PyTorch for distributed utilities and ShardTensor

Fixed

• EntryPoint error that occured during physicsnemo checkpoint loading

[1.0.0] - 2025-03-18

Added

• DoMINO model architecture, datapipe and training recipe
• Added matrix decomposition scheme to improve graph partitioning
• DrivAerML dataset support in FIGConvNet example.
• Retraining recipe for DoMINO from a pretrained model checkpoint
• Prototype support for domain parallelism of using ShardTensor (new).
• Enable DeviceMesh initialization via DistributedManager.
• Added Datacenter CFD use case.
• Add leave-in profiling utilities to physicsnemo, to easily enable torch/python/nsight profiling in all aspects of the codebase.

Changed

• Refactored StormCast training example
• Enhancements and bug fixes to DoMINO model and training example
• Enhancement to parameterize DoMINO model with inlet velocity
• Moved non-dimensionaliztion out of domino datapipe to datapipe in domino example
• Updated utils in physicsnemo.launch.logging to avoid unnecessary wandb and mlflow imports
• Moved to experiment-based Hydra config in Lagrangian-MGN example
• Make data caching optional in MeshDatapipe
• The use of older importlib_metadata library is removed

Deprecated

• ProcessGroupConfig is tagged for future deprecation in favor of DeviceMesh.

Fixed

• Update pytests to skip when the required dependencies are not present
• Bug in data processing script in domino training example
• Fixed NCCL_ASYNC_ERROR_HANDLING deprecation warning

Dependencies

• Remove the numpy dependency upper bound
• Moved pytz and nvtx to optional
• Update the base image for the Dockerfile
• Introduce Multi-Storage Client (MSC) as an optional dependency.
• Introduce wrapt as an optional dependency, needed when using ShardTensor's automatic domain parallelism

[0.9.0] - 2024-12-04

Added

• Graph Transformer processor for GraphCast/GenCast.
• Utility to generate STL from Signed Distance Field.
• Metrics for CAE and CFD domain such as integrals, drag, and turbulence invariances and spectrum.
• Added gradient clipping to StaticCapture utilities.
• Bistride Multiscale MeshGraphNet example.
• FIGConvUNet model and example.
• The Transolver model.
• The XAeroNet model.
• Incoporated CorrDiff-GEFS-HRRR model into CorrDiff, with lead-time aware SongUNet and cross entropy loss.
• Option to offload checkpoints to further reduce memory usage
• Added StormCast model training and simple inference to examples
• Multi-scale geometry features for DoMINO model.

Changed

• Refactored CorrDiff training recipe for improved usability
• Fixed timezone calculation in datapipe cosine zenith utility.
• Refactored EDMPrecondSRV2 preconditioner and fixed the bug related to the metadata
• Extended the checkpointing utility to store metadata.
• Corrected missing export of loggin function used by transolver model

[0.8.0] - 2024-09-24

Added

• Graph Transformer processor for GraphCast/GenCast.
• Utility to generate STL from Signed Distance Field.
• Metrics for CAE and CFD domain such as integrals, drag, and turbulence invariances and spectrum.
• Added gradient clipping to StaticCapture utilities.
• Bistride Multiscale MeshGraphNet example.

Changed

• Refactored CorrDiff training recipe for improved usability
• Fixed timezone calculation in datapipe cosine zenith utility.

[0.7.0] - 2024-07-23

Added

• Code logging for CorrDiff via Wandb.
• Augmentation pipeline for CorrDiff.
• Regression output as additional conditioning for CorrDiff.
• Learnable positional embedding for CorrDiff.
• Support for patch-based CorrDiff training and generation (stochastic sampling only)
• Enable CorrDiff multi-gpu generation
• Diffusion model for fluid data super-resolution (CMU contribution).
• The Virtual Foundry GraphNet.
• A synthetic dataloader for global weather prediction models, demonstrated on GraphCast.
• Sorted Empirical CDF CRPS algorithm
• Support for history, cos zenith, and downscaling/upscaling in the ERA5 HDF5 dataloader.
• An example showing how to train a "tensor-parallel" version of GraphCast on a Shallow-Water-Equation example.
• 3D UNet
• AeroGraphNet example of training of MeshGraphNet on Ahmed body and DrivAerNet datasets.
• Warp SDF routine
• DLWP HEALPix model
• Pangu Weather model
• Fengwu model
• SwinRNN model
• Modulated AFNO model

Changed

• Raise PhysicsNeMoUndefinedGroupError when querying undefined process groups
• Changed Indexing error in examples/cfd/swe_nonlinear_pino for physicsnemo loss function
• Safeguarding against uninitialized usage of DistributedManager

Removed

• Remove mlflow from deployment image

Fixed

• Fixed bug in the partitioning logic for distributing graph structures intended for distributed message-passing.
• Fixed bugs for corrdiff diffusion training of EDMv1 and EDMv2
• Fixed bug when trying to save DDP model trained through unified recipe

Dependencies

• Update DALI to CUDA 12 compatible version.
• Update minimum python version to 3.10

[0.6.0] - 2024-04-17

Added

• The citation file.
• Link to the CWA dataset.
• ClimateDatapipe: an improved datapipe for HDF5/NetCDF4 formatted climate data
• Performance optimizations to CorrDiff.
• Physics-Informed Nonlinear Shallow Water Equations example.
• Warp neighbor search routine with a minimal example.
• Strict option for loading PhysicsNeMo checkpoints.
• Regression only or diffusion only inference for CorrDiff.
• Support for organization level model files on NGC file system
• Physics-Informed Magnetohydrodynamics example.

Changed

• Updated Ahmed Body and Vortex Shedding examples to use Hydra config.
• Added more config options to FCN AFNO example.
• Moved posiitonal embedding in CorrDiff from the dataloader to network architecture

Deprecated

• physicsnemo.models.diffusion.preconditioning.EDMPrecondSR. Use EDMPecondSRV2 instead.

Removed

• Pickle dependency for CorrDiff.

Fixed

• Consistent handling of single GPU runs in DistributedManager
• Output location of objects downloaded with NGC file system
• Bug in scaling the conditional input in CorrDiff deterministic sampler

Dependencies

• Updated DGL build in Dockerfile
• Updated default base image
• Moved Onnx from optional to required dependencies
• Optional Makani dependency required for SFNO model.

[0.5.0] - 2024-01-25

Added

• Distributed process group configuration mechanism.
• DistributedManager utility to instantiate process groups based on a process group config.
• Helper functions to faciliate distributed training with shared parameters.
• Brain anomaly detection example.
• Updated Frechet Inception Distance to use Wasserstein 2-norm with improved stability.
• Molecular Dynamics example.
• Improved usage of GraphPartition, added more flexible ways of defining a partitioned graph.
• Physics-Informed Stokes Flow example.
• Profiling markers, benchmarking and performance optimizations for CorrDiff inference.
• Unified weather model training example.

Changed

• MLFLow logging such that only proc 0 logs to MLFlow.
• FNO given seperate methods for constructing lift and spectral encoder layers.

Removed

• The experimental SFNO

Dependencies

• Removed experimental SFNO dependencies
• Added CorrDiff dependencies (cftime, einops, pyspng, nvtx)
• Made tqdm a required dependency

[0.4.0] - 2023-11-20

Added

• Added Stokes flow dataset
• An experimental version of SFNO to be used in unified training recipe for weather models
• Added distributed FFT utility.
• Added ruff as a linting tool.
• Ported utilities from PhysicsNeMo Launch to main package.
• EDM diffusion models and recipes for training and sampling.
• NGC model registry download integration into package/filesystem.
• Denoising diffusion tutorial.

Changed

• The AFNO input argument img_size to inp_shape
• Integrated the network architecture layers from PhysicsNeMo-Sym.
• Updated the SFNO model, and the training and inference recipes.

Fixed

• Fixed physicsnemo.Module from_checkpoint to work from custom model classes

Dependencies

• Updated the base container to PyTorch 23.10.
• Updated examples to use Pydantic v2.

[0.3.0] - 2023-09-21

Added

• Added ability to compute CRPS(..., dim: int = 0).
• Added EFI for arbitrary climatological CDF.
• Added Kernel CRPS implementation (kcrps)
• Added distributed utilities to create process groups and orthogonal process groups.
• Added distributed AFNO model implementation.
• Added distributed utilities for communication of buffers of varying size per rank.
• Added distributed utilities for message passing across multiple GPUs.
• Added instructions for docker build on ARM architecture.
• Added batching support and fix the input time step for the DLWP wrapper.

Changed

• Updating file system cache location to physicsnemo folder

Fixed

• Fixed physicsnemo uninstall in CI docker image

Security

• Handle the tar ball extracts in a safer way.

Dependencies

• Updated the base container to latest PyTorch 23.07.
• Update DGL version.
• Updated require installs for python wheel
• Added optional dependency list for python wheel

[0.2.1] - 2023-08-08

Fixed

• Added a workaround fix for the CUDA graphs error in multi-node runs

Security

• Update certifi package version

[0.2.0] - 2023-08-07

Added

• Added a CHANGELOG.md
• Added build support for internal DGL
• 4D Fourier Neural Operator model
• Ahmed body dataset
• Unified Climate Datapipe

Changed

• DGL install changed from pypi to source
• Updated SFNO to add support for super resolution, flexible checkpoining, etc.

Fixed

• Fixed issue with torch-harmonics version locking
• Fixed the PhysicsNeMo editable install
• Fixed AMP bug in static capture

Security

• Fixed security issues with subprocess and urllib in filesystem.py

Dependencies

• Updated the base container to latest PyTorch base container which is based on torch 2.0
• Container now supports CUDA 12, Python 3.10

[0.1.0] - 2023-05-08

Added

• Initial public release.