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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.

[2.1.0a0] - 2026-XX-YY

Added

  • Adds GLOBE model (physicsnemo.experimental.models.globe.model.GLOBE), including new variant that uses a dual tree traversal algorithm to reduce the complexity of the kernel evaluations from O(N^2) to O(N).
  • Adds GLOBE AirFRANS example case (examples/cfd/external_aerodynamics/globe/airfrans)
  • Adds drop-test dynamics recipe.
  • Adds concrete dropout uncertainty quantification for GeoTransolver. Learnable per-layer dropout rates enable MC-Dropout inference for uncertainty estimates. Disabled by default (concrete_dropout: false).
  • Adds automatic support for FSDP and/or ShardTensor models in checkpoint save/load functionality
  • PhysicsNeMo-Mesh now supports conversion from PyVista/VTK/VTU meshes that may contain polyhedral cells.
  • In PhysicsNeMo-Mesh, adds Mesh.to_point_cloud(), .to_edge_graph(), and .to_dual_graph() methods. These allow Mesh conversion to 0D point clouds, 1D edge graphs, and 1D dual graphs, respectively, when connectivity information is not needed.
  • Adds physicsnemo.mesh.generate subpackage with marching_cubes for isosurface extraction from 3D scalar fields, returning a Mesh object. Supports the NVIDIA Warp backend.
  • Adds a type system to PhysicsNeMo-Mesh, allowing annotation of Mesh dimensions using notation like Mesh[2, 3] for a 2D manifold in 3D space.
  • Adds adjacency caching to PhysicsNeMo-Mesh Mesh objects, allowing efficient reuse of neighbor information.
  • Adds DomainMesh class for grouping an interior mesh with named boundary meshes and domain-level metadata, with passthrough geometric transforms (translate, rotate, scale, transform) and data operations.
  • Allows selective per-field transformation of Mesh objects: transform_point_data, transform_cell_data, and transform_global_data now accept bool | TensorDict (or plain dict for convenience).
  • Adds physicsnemo.mesh.remeshing subpackage with partition_cells() for creating Voronoi regions around seed points. BVH-accelerated.
  • Added support for 1D, 2D, and 3D neighborhood attention (natten) via physicsnemo.nn.functional interface, with full ShardTensor support.
  • Added derivative functionals in physicsnemo.nn.functional for uniform_grid_gradient, rectilinear_grid_gradient, spectral_grid_gradient, meshless_fd_derivatives, mesh_lsq_gradient, and mesh_green_gauss_gradient.
  • Adds physicsnemo.sym module for symbolic PDE residual computation (PhysicsInformer). Users define PDEs via SymPy and select a gradient method (autodiff, finite_difference, spectral, meshless_finite_difference, least_squares); spatial derivatives are computed automatically using the nn.functional.derivatives functionals.
  • Ports all physics-informed examples (LDC PINNs, Darcy, Stokes MGN, DoMINO, datacenter, xaeronet, MHD/SWE PINO) to the new physicsnemo.sym interface, replacing the separate physicsnemo-sym package dependency. Geometry is now handled via physicsnemo.mesh and PyVista.
  • Added geometry functionals in physicsnemo.nn.functional for mesh_poisson_disk_sample, mesh_to_voxel_fraction, and signed_distance_field.
  • Adds embedded OOD guardrail OODGuard at physicsnemo.experimental.guardrails.embedded, optionally wired into GeoTransolver via a new guard_config constructor argument. The guard calibrates per-channel global bounds and a geometry-latent kNN threshold during training, and emits warnings on out-of-distribution inputs at inference.
  • In PhysicsNeMo-Mesh, physicsnemo.mesh.geometry now publicly exposes stable_angle_between_vectors and compute_triangle_angles (previously only available via the private physicsnemo.mesh.curvature._utils).
  • PhysicsNeMo Datapipes enables reproducability through torch.generator utilities.
  • PhysicsNeMo Datapipes now supports physicsnemo.mesh.Mesh and physicsnemo.mesh.DomainMesh objects for deserialization, with transformations and utilities for mesh-based datasets.
  • PhysicsNeMo Datapipes now support MultiDataset construction, allowing on-the-fly construction of multi-source composite datasets that can be sampled and processed efficiently and coherently as one dataset.
  • PhysicsNeMo Datapipes also support random augmentations for mesh-based datapipes, leveraging torch.distributions for broad random distribution support. Mesh and DomainMesh datasets allow random translation, scaling, and rotation of mesh data in coherent ways, compatible with reproducability features of physicsnemo datapipes.
  • Adds a new unified training recipe for external aerodynamics that supports training on multiple datasets (DrivaerML, ShiftSUV, HighLiftAeroML, or more, bring your own, mix and match), supports training several different models (Domino, Transolver, GeoTransolver, Flare, GeoTransolver with Flare-attention, bring your own!). Leverages mesh datasets and non-dimensionalization to enable dataset mixing and matching at runtime. Train with surface or volume data.
  • Adds a new physicsnemo.diffusion.multi_diffusion subpackage that scales 2D diffusion models to large domains via patch-based training and inference. Provides MultiDiffusionModel2D (wraps a base model and handles state patching, conditioning preprocessing, positional-embedding injection, and per-patch output fusion), the MultiDiffusionMSEDSMLoss / MultiDiffusionWeightedMSEDSMLoss losses for patch-based DSM training, and MultiDiffusionPredictor for sampling (plugs straight into sample() / get_denoiser() and the standard solvers). Patching primitives (BasePatching2D, GridPatching2D, RandomPatching2D) are exposed under the same subpackage and are torch.compile-friendly with fullgraph=True.
  • Adds "epsilon" as a supported prediction type throughout the diffusion framework, alongside the existing "x0" and "score" modes. A new PredictorType = Literal["x0", "score", "epsilon"] alias in physicsnemo.diffusion.base is wired through losses (MSEDSMLoss, WeightedMSEDSMLoss, and the multi-diffusion losses), preconditioners, samplers / solvers, DPS guidance, and noise schedulers, enabling end-to-end training and sampling of epsilon-parameterized models. Losses gain an epsilon_to_x0_fn kwarg used for the epsilon-to-x0 conversion required during DSM training.
  • Added support for Batched radius search, which enables Domino and GeoTransolver with local features and batch size > 1.

Changed

  • Improved crash recipe with configurable stats directory.
  • physicsnemo.mesh.sampling.find_nearest_cells uses a KNN-backed implementation, and no longer accepts the bvh=, chunk_size=, max_rounds=, or max_candidates_per_point= parameters.
  • ⚠️ BC-impact (deep imports): internal physicsnemo.nn.functional modules were reorganized by category. Public top-level functional imports are unchanged, but code importing internal module paths directly (for example physicsnemo.nn.functional.knn or physicsnemo.nn.functional.radius_search) should migrate to physicsnemo.nn.functional.neighbors.*.
  • Consolidated Warp interpolation kernels for grid-to-point and point-to-grid backends, and added missing kernel/helper docstrings.
  • In PhysicsNeMo-Mesh, dual-mesh primitives gained closed-form fast paths for triangle meshes embedded in 3D. compute_circumcenters is up to ~10000x faster (e.g. 11 s -> ~1 ms on a 360 K-triangle AirFRANS mesh, RTX 4090) by replacing batched torch.linalg.lstsq over (2, 3) systems with a closed-form cross product, and compute_vertex_angles is up to ~15x faster on the same meshes by replacing the dimension-agnostic Gram-determinant formula with an atan2(||cross||, dot) formulation. Anything that depends on these (Gaussian curvature, FEM Laplacian, cotangent weights, Voronoi areas, smoothing) inherits the speedup. See perf.md for the full audit.
  • In PhysicsNeMo-Mesh, BVH construction is faster on GPU. _compute_morton_codes has a CUDA-specific fused-bits path that eliminates the n_bits sequential kernel launches of the previous bit-loop (5-8x speedup on small / medium meshes), and BVH.from_mesh reuses the cached Mesh.cell_centroids instead of recomputing. End-to-end BVH.from_mesh is ~2x faster on a 162 K-tet cube_volume mesh.
  • In PhysicsNeMo-Mesh, the topology-dedup APIs (categorize_facets_by_count, find_edges_in_reference, remove_duplicate_cells, build_adjacency_from_pairs) gained optional index_bound / n_targets parameters. When the caller passes a strict upper bound (typically mesh.n_points or mesh.n_cells), the implicit tensor.max().item() GPU sync is avoided and the dedup uses a packed int64 unique (via the new internal unique_index_tuples) and a single composite-key argsort. End-to-end get_boundary_edges and cell_to_cells_adjacency are ~2x faster on practical-size unstructured meshes (e.g. 360 K-triangle AirFRANS).
  • ⚠️ BC-impact (deep imports): in PhysicsNeMo-Mesh, stable_angle_between_vectors and compute_triangle_angles moved from physicsnemo.mesh.curvature._utils to physicsnemo.mesh.geometry._angles. The old private path is no longer available; use the physicsnemo.mesh.geometry re-export instead.
  • Refactored the patching utilities under physicsnemo.diffusion.multi_diffusion.patching. Patching and fusion operations are now more performant and torch.compile-friendly (e.g. fullgraph=True,error_on_recompile=True).
  • Refactored the examples/geophysics/diffusion_fwi full-waveform inversion example to use the consolidated physicsnemo.diffusion API (preconditioners, samplers, losses, DPS guidance) and removed the recipe-local copies of these utilities under utils/.
  • Refactored the examples/generative/topodiff recipe to use the consolidated physicsnemo.diffusion API (MSEDSMLoss with prediction_type="epsilon", sample(), DPSScorePredictor) plus a recipe-local DDPM scheduler, solver, and classifier guidance. Removed the now-unused Diffusion, DatasetTopoDiff, and load_data_topodiff abstractions from physicsnemo.models.topodiff.
  • Significantly expanded CI test coverage for physicsnemo.diffusion, including new tests for samplers, solvers, preconditioners, losses, DPS guidance, multi-diffusion, and patching utilities, plus combined-workflow and from-checkpoint round-trip tests. Most tests run with fullgraph=True and error_on_recompile to catch torch.compile regressions.

Deprecated

  • physicsnemo.utils.mesh is deprecated and will be removed in v2.2.0. For isosurface extraction, use physicsnemo.mesh.generate.marching_cubes instead of sdf_to_stl. For VTP/OBJ/STL file conversion (combine_vtp_files, convert_tesselated_files_in_directory), use VTK or PyVista directly.

Removed

Fixed

  • Fixed functional benchmark plot fallback labeling so unlabeled ASV results use the same key ordering as the benchmark runner.
  • Fixed graph break caused by FunctionSpec dispatch (max(key=) is not supported by torch.compile)
  • Fixed bug in Pangu, FengWu attention window shift for asymmetric longitudes
  • Fixed a bug in mesh.sampling.find_nearest_cells, where a mixup between L2 and L-inf norms could cause slightly incorrect nearest-neighbor assignments in highly skewed meshes.
  • Fixed TensorDict key-ordering bug in GLOBE's Barnes-Hut kernel that caused incorrect results when tensordict >= 0.12 reordered leaves during TensorDict construction from dict literals mixing plain and nested keys.
  • In PhysicsNeMo-Mesh, from_pyvista now correctly handles UnstructuredGrid inputs in newer pyvista versions, looking up cell-type buckets in cells_dict with np.uint8(pv.CellType.X) keys rather than the IntEnum value, and skipping non-numeric VTK arrays (strings, objects) when copying point / cell / field data into the Mesh TensorDicts instead of failing the conversion.
  • In PhysicsNeMo-Mesh, safe_eps(dtype) is now capped at torch.finfo(dtype).eps, which fixes a float16 corner case where the previous tiny ** 0.25 floor exceeded machine epsilon and could corrupt fp16 mesh quantities. Ad-hoc + 1e-10 denominators in smooth_laplacian and compute_quality_metrics have been replaced with the dtype-aware .clamp(min=safe_eps(dtype)) to avoid silently zeroing fp16 weights.
  • Fixed a silent bug in loading of optimizer state from checkpoint for FSDP-backed models with use_orig_params=False and channels last memory format.
  • Fixed issues with physicsnemo.nn.functional's radius_search that caused crashes when used with torch.compile.
  • Fixed the sinusoidal positional embeddings formula in SongUNet and MultiDiffusionModel2D so it now follows the standard sin / cos convention. Affected reference data was regenerated.

Security

Dependencies

  • Increments minimum viable PyTorch version to torch>=2.5.0 to support FSDP better

[2.0.0] - 2026-03-09

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 physicsnemo.experimental.nn.symmetry module that implements building blocks that preserve 2D and 3D rotational equivariance using a grid-based layout for efficient GPU parallelization, and an emphasis on compact einsum operations.
  • Flare attention support for both Transolver and GeoTransolver models.

Changed

  • PhysicsNemo v2.0 contains significant reorganization of tools. Please see the v2.0-MIGRATION-GUIDE.md to understand what has changed and why.
  • DiT (Diffusion Transformer) has been moved from physicsnemo.experimental.models.dit to physicsnemo.models.dit.

Fixed

  • Shape mistmatch bug in the Lennard Jones example

Dependencies

  • CUDA backend is now selected via orthogonal cu12 / cu13 extras rather than being hardcoded to CUDA 13. Feature extras (nn-extras, utils-extras, etc.) are now CUDA-agnostic and can be combined with either backend, e.g. pip install "nvidia-physicsnemo[cu13,nn-extras]". When neither cu12 nor cu13 is specified, PyTorch is installed from PyPI using its default build (currently CUDA 12.8 on Linux). For development with uv, use uv sync --extra cu13 (or --extra cu12) to select the backend.

[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.