navier-cfd 0.2.0

CFD-first neural solver registry, Hugging Face data hub, benchmark toolkit, evidence-aware task recommender, and agentic experiment planner.

NAVIER-CFD

Neural and Agentic Verification, Integration, Evaluation, and Recommendation for Computational Fluid Dynamics

NAVIER-CFD is a CFD-first Python platform and project website for neural PDE solvers, hybrid numerical acceleration, benchmark datasets, paper-evidence-aware task recommendation, Hugging Face integration, and agentic experiment planning.

Project website and interactive tool

• Project website: https://samsomyajit.github.io/NAVIER-CFD/
• Interactive model and dataset recommender: https://samsomyajit.github.io/NAVIER-CFD/recommender/
• Technical documentation: https://samsomyajit.github.io/NAVIER-CFD/docs/

The web recommender runs entirely in the browser. It applies hard compatibility filters, explains every score, recommends suitable benchmark datasets, and exports a reproducible run manifest.

A recommendation is a task-specific hypothesis, not proof that one architecture is universally superior. Report the final score together with evidence confidence, metric coverage, matched paper records, and the target benchmark.

Evidence-aware recommendation

Version 0.2 introduces a traceable paper-result layer. Each quantitative claim is stored with the paper, benchmark, metric, baseline, physical regime, dimension, mesh, geometry, temporal mode, fidelity, code/data availability, evidence level, and caveats.

The recommender now combines:

1. Hard compatibility filtering for dimension, representation, geometry/mesh transfer, numerical role, temporal mode, and hardware.
2. Task-to-paper similarity across physics, discretization, geometry, temporal regime, role, and fidelity.
3. Metric-aware utility that prefers same-pipeline baseline comparisons and refuses to compare scale-dependent absolute MSE values across unrelated datasets.
4. Evidence quality weighting for independent reproduction, peer review, code/data availability, cases, seeds, and baseline quality.
5. Bayesian shrinkage toward a neutral prior when evidence is sparse.
6. Confidence and coverage reporting so a high score with one weak paper cannot masquerade as a mature result.

Citation counts, venue prestige, author prestige, and coauthorship centrality are deliberately excluded from performance scoring. Bibliometrics are used for paper discovery and provenance, not as a proxy for CFD accuracy.

See Evidence Scoring and the frozen catalog at src/navier_cfd/data/paper_evidence.json.

Scientific pipeline

The workflow keeps the learned numerical role explicit—surrogate, closure, corrector, preconditioner, inverse model, controller, or generator—and connects each experiment to versioned data, traceable model cards, CFD-aware metrics, paper evidence, and a reproducible run manifest.

Why NAVIER-CFD

• 55-model taxonomy: acceleration frameworks, surrogates, general PDE solvers, specialized CFD, geometry and unstructured-mesh models, foundation models, inverse methods, uncertainty, particle and multiphase models, and generative methods.
• 11 first-class datasets: PDEBench, CFDBench, RealPDEBench, AirfRANS, DrivAerNet++, DrivAerML, The Well, APEBench, ScalarFlow, ShapeNet-Car, and EAGLE.
• Paper-level evidence: traceable benchmark claims with task context, provenance, quality, confidence, and comparability limits.
• Full Hugging Face support: discovery, inspection, selective downloads, revision pinning, authentication, streaming, caching, and arbitrary CFD dataset identifiers.
• Explainable recommendation: compatibility filtering plus evidence-aware ranking by physics, dimension, mesh, geometry, temporal regime, numerical role, fidelity, memory, conservation, uncertainty, and transfer requirements.
• Agentic AI: deterministic offline planning and provider-neutral interfaces for external LLM agents.
• CFD-aware benchmarking: field, spectral, rollout, conservation, OOD, quantity-of-interest, uncertainty, wall-clock, memory, and break-even metrics.
• Safe integration: external repositories remain metadata-first and are never executed automatically.

Installation

From PyPI:

pip install navier-cfd

From source:

git clone https://github.com/Samsomyajit/NAVIER-CFD.git
cd NAVIER-CFD
pip install -e .

# development, tests, and documentation
pip install -e ".[dev,docs]"

Quick start

# Explore catalogs
navier models list
navier models list --category acceleration
navier datasets list

# Inspect registered paper evidence
navier evidence list
navier evidence list --model-id gino
navier evidence coverage

# Search Hugging Face
navier datasets discover "computational fluid dynamics" --limit 20

# Download a CFDBench subset
navier datasets download cfdbench \
  --local-dir ./data/cfdbench \
  --pattern "cylinder/**"

# Evidence-aware recommendation
navier recommend \
  --problem vehicle_drag \
  --task surrogate \
  --dimension 3 \
  --mesh point_cloud \
  --temporal steady \
  --geometry varying \
  --physics aerodynamics \
  --fidelity rans \
  --memory-gb 80 \
  --top-k 10

# Generate an agentic experiment plan
navier agent plan \
  "Benchmark RealPDEBench cylinder sim-to-real forecasting with 24 GB VRAM, conservation and uncertainty"

Python API

from navier_cfd import Catalog, TaskSpec, recommend_models

catalog = Catalog.load_builtin()
task = TaskSpec(
    problem="3d_vehicle_aerodynamics",
    task_type="surrogate",
    dimension=3,
    mesh_type="point_cloud",
    temporal_mode="steady",
    geometry_mode="varying",
    physics=("aerodynamics",),
    fidelity="rans",
    requires_geometry_transfer=True,
    requires_mesh_transfer=True,
    hardware_memory_gb=80,
)

for result in recommend_models(task, catalog.models, top_k=8):
    print(result.model.name, result.score)
    print("evidence:", result.evidence_score)
    print("confidence:", result.evidence_confidence)
    print("coverage:", result.evidence_coverage)
    print("records:", result.evidence_count)
    print("reasons:", result.reasons)
    print("cautions:", result.cautions)

Registered model families

• Physics-informed: PINN, NSFnets, PINNsFormer, PINO, PI-MFM, and RiemannONet.
• Operator learning: DeepONet, MIONet, Fourier-DeepONet, Fourier-MIONet, FNO, F-FNO, U-FNO, U-NO, LSM, MWT, Laplace NO, and state-space NO.
• Geometry and transformer solvers: Geo-FNO, GINO, GNOT, Transolver, UPT, MeshGraphNets, DoMINO, and ReViT.
• CFD-specialized: PIBERT, FourierFlow, P3D, AeroTransformer, NeuralDEM, DeepM&Mnet, and Energy Transformer.
• Foundation and generative: DPOT, Poseidon, PROSE-FD, BCAT, PDEformer-1, Tadpole, PDE-Refiner, FunDiff, and Flow Matching for PDEs.
• Acceleration: Solver-in-the-Loop, INC, PICT, diffSPH, NeuroSEM, neural-operator preconditioned Newton, and geometry-aware neural preconditioning.
• Uncertainty and adaptation: Conformalized-DeepONet and TANTE.

“Included” means represented through a uniform model card and recommendation interface. Official implementations remain external so upstream licenses and revisions are respected.

Repository map

src/navier_cfd/
  agents/          deterministic and LLM-ready planning
  benchmarks/      CFD metrics and benchmark plans
  datasets/        Hugging Face discovery, download, streaming
  models/          safe adapter protocol
  data/            frozen paper-evidence catalog
  catalogs.py      model and dataset registries
  evidence.py      task similarity, quality, utility, shrinkage
  recommender.py   compatibility prior + evidence ranking

website/
  index.html       standalone project website
  recommender/     interactive browser recommender
  data/            static model and dataset catalogs

docs/              MkDocs technical documentation
case_studies/      detailed benchmark study guides
configs/tasks/     reusable task specifications

Recommender validation

The Python recommender is covered by pytest; the browser engine is covered by Node's built-in test runner.

pytest tests/test_recommender.py tests/test_evidence_recommender.py
node --test website/recommender/recommender-core.test.mjs

Canonical tests verify task-specific evidence transfer, geometry-aware 3D ranking, hybrid acceleration selection, evidence provenance, and neutral treatment of incomparable absolute MSE values.

Packaging and deployment

• Package: navier-cfd
• Import namespace: navier_cfd
• Current version: 0.2.0
• Evidence algorithm: 0.2.0-evidence
• Build backend: Hatchling
• Website source: website/
• Documentation source: docs/
• Branch publisher: .github/workflows/pages-branch.yml
• PyPI Trusted Publishing workflow: .github/workflows/publish-pypi.yml

License

Licensed under the Apache License 2.0. Cite NAVIER-CFD and the original model, dataset, upstream implementation, benchmark, and numerical-solver references used in every experiment.