awesome-ai-cae

Contents

• Quickstart
• AI-Readiness Index
• How the Score Works
• Core Engine Readiness
• MCP Servers
• CFD — Computational Fluid Dynamics
• FEA — Finite Element Analysis
• SPH — Smoothed Particle Hydrodynamics
• DEM — Discrete Element Method
• Visualization & Post-processing
• CAD & Geometry
• Mesh Generation
• Differentiable Simulation
• AI/ML for Simulation
• Surrogate Models & PINNs
• Optimization
• Data Formats & I/O
• Datasets & Benchmarks
• Learning Resources
• Star History

Quickstart — give your agent a CAE tool

Three tools here ship a Model Context Protocol server, so an agent (Claude Desktop, Cursor, Cline…) drives them with zero glue code. Add one to your MCP client config:

{
  "mcpServers": {
    "viznoir": { "command": "uvx", "args": ["viznoir"] }
  }
}

MCP server	Ask your agent	Install
viznoir	"Render this OpenFOAM case as a cinematic volume animation."	uvx viznoir
ParaView-MCP	"Open this VTK file, color by pressure, screenshot it."	see repo
OpenFOAM-MCP	"Set up a pipe-flow case and explain the turbulence model."	see repo

_{Exact launch command lives in each server’s README. No MCP? Every other tool is Python/CLI-scriptable — your agent calls it the same way you would.}

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AI-Readiness Index

The headline metric: tools ranked by agent-callability — MCP, Python API, CLI, maintenance — not stars. Auto-updated weekly by readiness-score.py. Full table: READINESS.md · machine-readable: data/readiness.json.

#	Score	Grade	Tool	Interfaces	⭐
🥇	95	🟢 AI-Native	llnl/paraview_mcp	MCP, Python, pip	69
🥈	93	🟢 AI-Native	kimimgo/viznoir	MCP, Python, pip ✅	19
🥉	65	🔵 Agent-Ready	taichi-dev/taichi	Python, pip ✅	28,409
4	65	🔵 Agent-Ready	google-deepmind/mujoco	Python, pip	15,630
5	65	🔵 Agent-Ready	rerun-io/rerun	Python, pip	11,583
6	65	🔵 Agent-Ready	google-deepmind/graphcast	Python, pip	7,705
7	65	🔵 Agent-Ready	NVIDIA/warp	Python, pip	7,185
8	64	🔵 Agent-Ready	CadQuery/cadquery	Python, pip ✅	5,921
9	64	🔵 Agent-Ready	libigl/libigl	Python, pip	5,105
10	64	🔵 Agent-Ready	PolymathicAI/the_well	Python, pip	4,468
11	64	🔵 Agent-Ready	lululxvi/deepxde	Python, pip	4,463
12	64	🔵 Agent-Ready	NeuralOperator/neuraloperator	Python, pip	3,952
13	64	🔵 Agent-Ready	pyvista/pyvista	Python, pip ✅	3,845
14	64	🔵 Agent-Ready	mikedh/trimesh	Python, pip ✅	3,697
15	64	🔵 Agent-Ready	meta-pytorch/botorch	Python, pip	3,618

_{🟢 2 AI-Native · 🔵 61 Agent-Ready · 🟡 28 Scriptable · ⚪ 23 Experimental — across 114 ranked tools (updated 2026-10-12). ✅ = install + import execution-verified. Full ranking →}

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How the Score Works

The AI-Readiness Score (0–100) ranks tools by how directly an autonomous agent can drive them — callability over popularity.

Signal	Points	Why it matters to an agent
MCP server	35	The agent calls it with zero glue code
Python API	25	Native scripting
CLI / REST	15	Headless automation
pip-installable	+15	One-command install (verified on PyPI)
Maintained (<6 mo)	15	Won't rot mid-project
Adoption (stars)	10	log-scaled — popularity barely moves the needle

The five base signals (MCP + Python + CLI + Maintained + Adoption) total 100; pip is an additive bonus, and the final score is capped at 100. So a tool can reach 100 several ways, but only MCP servers clear the AI-Native bar.

Grades:

Scores regenerate weekly from README.md via readiness-score.py — fully reproducible, no hand-tuning. Open a PR adding a tool and a bot scores it automatically.

Verified vs declared

Honest about what’s checked.

Verified (objective, reproducible) — live GitHub stars/activity; PyPI availability; and an install + import smoke-test (verify_install.py → data/verified.json) that spins up an isolated uv venv, runs pip install + import, and records the result. Tools that pass are marked ✅ in the Index. Current run: 8/10 flagship tools pass; the 2 misses are recorded honestly — Gmsh needs a system GL lib, DeepXDE needs a chosen backend.

Declared (from the entry’s tags) — MCP and CLI/API. We link the server/CLI; we don’t yet replay an end-to-end agent call.

Roadmap (deepening the moat) — execution-verified MCP handshakes and headless runs, plus a per-tool agent-call transcript, so the score reflects tools an agent has actually driven, not just ones that expose an interface.

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Core Engine Readiness

A hand-curated editorial deep-dive on 17 foundational solvers — capability columns (Python binding, headless, Docker, AI-native) reflect maintainer judgment, ⭐ is live. This complements the auto-generated Index above, which stays the single source of truth for scores. Only 2 engines have MCP integration today.

Engine	Domain	⭐	Python API	Headless	Docker	🤖 AI-Native
OpenFOAM	CFD		PyFoam	✅	✅	✅ Foam-Agent, MCP
FEniCS	FEA		✅ Native	✅	✅	—
Gmsh	Mesh	—	✅ Native	✅	✅	—
VTK / ParaView	Viz		✅ Native	✅	✅	✅ ParaView-MCP
SU2	CFD		pySU2	✅	✅	—
MFEM	FEA		PyMFEM	✅	✅	—
deal.II	FEA		Limited	✅	✅	—
DualSPHysics	SPH		Inductiva API	✅	✅	—
Taichi	Diff. Sim		✅ Native	✅	✅	—
PyFR	CFD		✅ Native	✅	✅	—
CalculiX	FEA	—	pycalculix	✅	✅	—
Elmer	FEA		PyElmer	✅	✅	—
OpenCASCADE	CAD		pythonOCC	✅	✅	—
MOOSE	FEA		Python	✅	✅	—
FreeFEM	FEA		FreeFem++	✅	✅	—
SfePy	FEA		✅ Native	✅	✅	—
MuJoCo	Diff. Sim		✅ Native	✅	✅	—

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MCP Servers

AI agents call these directly via Model Context Protocol.

• kimimgo/viznoir Python MCP - Cinema-quality science visualization. 22 tools for rendering, slicing, contouring, volume rendering, and animating OpenFOAM/VTK/CGNS data via VTK. Headless EGL/OSMesa.
• llnl/paraview_mcp Python MCP - Natural language control of ParaView via MCP. Multimodal LLM observes viewport for visual feedback (LLNL).
• webworn/openfoam-mcp-server C++ MCP - OpenFOAM MCP server with Socratic questioning for CFD education and expert error resolution.

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CFD — Computational Fluid Dynamics

Open-source solvers for fluid flow, heat transfer, and multiphysics.

• OpenFOAM/OpenFOAM-dev C++ - The open source CFD toolbox. Finite volume solvers for incompressible/compressible flow, multiphase, combustion, heat transfer.
• su2code/SU2 C++ Python - Multiphysics simulation and design optimization. Compressible/incompressible flow, structural analysis, adjoint-based design.
• LLNL/Nek5000 Fortran - High-order spectral element CFD solver. DNS/LES of turbulent flows. Scalable to millions of cores.
• Nek5000/nekRS C++ CUDA - GPU-accelerated spectral element CFD. Successor to Nek5000 with native CUDA/HIP/OpenCL support.
• precice/precice C++ Python - Coupling library for multi-physics simulations. Fluid-structure interaction, conjugate heat transfer.
• ProjectPhysX/FluidX3D C++ CUDA - GPU-accelerated Lattice Boltzmann fluid simulator. Real-time 3D visualization, scriptable via Python subprocess, supports multi-GPU.
• PyFR/PyFR Python - High-order flux reconstruction CFD on mixed unstructured grids. GPU-accelerated (CUDA/OpenCL/HIP).

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FEA — Finite Element Analysis

Structural, thermal, and multiphysics FEM solvers.

• CalculiX Fortran C - Free 3D structural FEM. Linear/nonlinear static, dynamic, thermal analysis. Abaqus INP compatible.
• dealii/dealii C++ - Adaptive finite elements. Supports hp-refinement, multigrid, and parallel distributed computing.
• ElmerCSC/elmerfem Fortran C++ - Multiphysics FEM solver. Fluid dynamics, structural mechanics, electromagnetics, heat transfer. CSC Finland.
• FEniCS/dolfinx C++ Python - Next-generation FEniCS. Automated PDE solving with high-level Python/C++ interface. Parallel, scalable.
• firedrakeproject/firedrake Python - Automated FEM with code generation from high-level problem descriptions. UFL domain-specific language.
• FreeFem/FreeFem-sources C++ - Partial differential equation solver using finite element method. High-level scripting language for 2D/3D problems.
• idaholab/moose C++ Python - Multiphysics Object-Oriented Simulation Environment. Coupled physics FEM framework from Idaho National Lab.
• KratosMultiphysics/Kratos C++ Python - Framework for multi-physics FEM. Structural, fluid, thermal, contact, FSI.
• mfem/mfem C++ - High-order finite element library. Supports GPU acceleration, AMR, and dozens of physics applications.
• OpenSees/OpenSees C++ - Open system for earthquake engineering simulation. Structural and geotechnical response analysis. Berkeley.
• sfepy/sfepy Python - Simple Finite Elements in Python. Solve PDEs by FEM in 1D, 2D, and 3D with plain Python scripting.

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SPH — Smoothed Particle Hydrodynamics

Meshless particle methods for free-surface flows and fluid-structure interaction.

• DualSPHysics/DualSPHysics C++ CUDA - GPU-accelerated SPH solver. Free-surface flows, wave generation, fluid-structure interaction, floating bodies.
• InteractiveComputerGraphics/SPlisHSPlasH C++ - Physically-based SPH fluid simulation. DFSPH, IISPH, PBF pressure solvers. Viscosity, surface tension.
• pypr/pysph Python Cython - SPH framework in Python. Compressible/incompressible flows, solid mechanics, coupled problems.

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DEM — Discrete Element Method

Particle-based simulation of granular materials, powders, and coupled particle-fluid systems.

• CFDEMproject/LIGGGHTS-PUBLIC C++ - Industry-standard open-source DEM for granular materials. LAMMPS-based with heat transfer and CFD coupling.
• lammps/lammps C++ Python - Large-scale Atomic/Molecular Massively Parallel Simulator. Classical MD and DEM with granular package. Sandia National Labs.
• MercuryDPM C++ - Open-source DEM for granular and particle-laden flows. Coarse-graining, contact models, and the MercuryCG analysis toolkit.
• SudoDEM/SudoDEM C++ Python - DEM for non-spherical particles. Polyhedra, super-ellipsoids, and cylinders for realistic granular simulations.
• Yade C++ Python - Extensible open-source DEM framework. Python scripting, deformable particles, coupled DEM-FEM and DEM-fluid problems.

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Visualization & Post-processing

Rendering, plotting, and interactive exploration of simulation results.

• fury-gl/fury Python - Free Unified Rendering in Python. VTK-based scientific visualization, 3D animations, and streamline rendering with a NumPy-friendly API.
• InsightSoftwareConsortium/itkwidgets Python - Interactive Jupyter widgets for 3D visualization of images, point sets, and meshes. Built on ITK and vtk.js.
• kimimgo/viznoir Python MCP - Cinema-quality science visualization MCP server. 22 tools, EGL/OSMesa headless, cinematic lighting, physics animations.
• Kitware/ParaView C++ Python - Multi-platform data analysis and visualization. VTK-based GUI + Python scripting + client-server architecture.
• Kitware/trame Python - Build interactive scientific web applications purely in Python. Integrates VTK and ParaView for server-side or local 3D rendering.
• Kitware/VolView TypeScript - Browser-based 3D radiological viewer for DICOM. Volume rendering, annotations, and measurements that run fully client-side.
• Kitware/VTK C++ Python - The Visualization Toolkit. 3D computer graphics, image processing, scientific visualization. Industry standard.
• Kitware/vtk-js JavaScript - Visualization Toolkit for the Web. WebGL/WebGPU scientific visualization and volume rendering entirely in the browser.
• marcomusy/vedo Python - Scientific analysis and visualization of 3D objects and point clouds. VTK-based with simple API.
• napari/napari Python - Fast n-dimensional image viewer. Plugin ecosystem for biomedical and scientific imaging.
• nmwsharp/polyscope C++ Python - Lightweight 3D viewer for meshes, point clouds, and scalar fields. One-line visualization for geometry processing.
• plotly/plotly.py Python - Interactive, publication-quality graphs. 3D scatter, surface, mesh, volume. Web-based rendering.
• pyvista/pyvista Python - Pythonic VTK. Streamlined 3D plotting, mesh analysis, and interactive visualization.
• rerun-io/rerun Rust Python - Multi-modal data logging and visualization SDK. Stream, store, and replay simulation data with Python API.

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CAD & Geometry

Parametric modeling, geometry processing, and CAD data exchange.

• CadQuery/cadquery Python - Parametric 3D CAD scripting. Build models with Python, export STEP/STL/IGES. OpenCASCADE kernel.
• CadQuery/OCP C++ Python - Python wrapper for OpenCASCADE via pybind11. Low-level foundation for CadQuery and build123d.
• FreeCAD/FreeCAD C++ Python - Open-source parametric 3D CAD modeler. Part design, FEM workbench, BIM, path (CAM).
• gumyr/build123d Python - Modern Python CAD with algebraic geometry API. Successor to CadQuery with cleaner builder pattern.
• mikedh/trimesh Python - Load and manipulate triangular meshes. Boolean operations, ray tracing, convex hulls, format conversion.
• nschloe/pygmsh Python - Python interface for Gmsh. Scripted geometry + mesh generation with parametric control.
• Open-Cascade-SAS/OCCT C++ - Open CASCADE Technology. Kernel for 3D surface and solid modeling, CAD data exchange (STEP/IGES).
• SolidCode/SolidPython Python - Python frontend for OpenSCAD. Generate 3D models programmatically with CSG operations.

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Mesh Generation

Structured, unstructured, and AI-driven mesh generation for simulation preprocessing.

• buaacyw/MeshAnything Python - Artist-quality mesh generation with autoregressive transformers. Any 3D input to mesh (ICLR 2025 spotlight).
• buaacyw/MeshAnythingV2 Python - Adjacent Mesh Tokenization for efficient artist-quality mesh generation. Faster and higher-quality than V1 (ICCV 2025).
• CGAL/cgal C++ - Computational Geometry Algorithms Library. Mesh generation, triangulation, Boolean operations, convex hulls.
• Gmsh C++ Python - Full-featured 3D finite element mesh generator. CAD engine, structured/unstructured meshing, built-in post-processing.
• libigl/libigl C++ Python - Header-only geometry processing library. Mesh parameterization, deformation, Boolean ops. Eurographics award winner.
• MmgTools/mmg C - Anisotropic mesh adaptation for 2D/3D surface and volume remeshing. Metric-based automatic refinement.
• NGSolve/netgen C++ Python - Automatic 3D tetrahedral mesh generator. CAD (OCC) integration, mesh optimization, parallel meshing.
• nmwsharp/geometry-central C++ - Applied geometry algorithms for surfaces and volumes. Geodesics, vector fields, intrinsic triangulations.
• OpenMeshLab/MeshXL Python - Foundation model for 3D mesh generation. Pre-trained on Objaverse, text-to-mesh capable (NeurIPS 2024).
• PyMesh/PyMesh Python C++ - Geometry processing library. Boolean, convex hull, remeshing, self-intersection repair.
• pyvista/tetgen C++ Python - Python interface to TetGen tetrahedral mesh generator. Constrained Delaunay tetrahedralization with quality control.
• wildmeshing/fTetWild C++ - Fast and robust tetrahedral meshing. Handles self-intersections and degenerate input. Ten times faster than TetWild.

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Differentiable Simulation

GPU-native frameworks for gradient-based optimization through physics.

• Autodesk/XLB Python JAX - Differentiable Lattice Boltzmann for physics-ML. Scales to billions of cells on multi-GPU.
• google/brax Python JAX - Massively parallel rigidbody physics on accelerator hardware. Millions of steps/second on TPU.
• jax-md/jax-md Python JAX - Differentiable, hardware-accelerated molecular dynamics. Runs on CPU/GPU/TPU via XLA.
• gbionics/jaxsim Python JAX - Differentiable multibody dynamics engine. Hardware-accelerated robot learning and control via JAX.
• google-deepmind/mujoco C++ Python - Multi-joint dynamics with contact. General-purpose physics engine for robotics, biomechanics, and control.
• NVIDIA/warp Python CUDA - Differentiable simulation and spatial computing. Reverse-mode AD, PyTorch/JAX interop.
• rtqichen/torchdiffeq Python PyTorch - ODE and SDE solvers with automatic differentiation. Adjoint-based backpropagation through continuous-time dynamics.
• taichi-dev/taichi Python CUDA - Productive GPU programming with automatic differentiation. DiffTaichi for differentiable physics.
• tumaer/JAXFLUIDS Python JAX - Fully-differentiable CFD solver for 3D compressible single-phase and two-phase flows.

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AI/ML for Simulation

Neural operators, LLM agents, and foundation models for computational engineering.

• csml-rpi/Foam-Agent Python API - AI agent for automated CFD workflows. LLM-driven OpenFOAM simulation setup and execution.
• deepmodeling/deepmd-kit Python C++ - Deep learning for molecular dynamics. Neural network potentials for large-scale atomistic simulations.
• dynamicslab/pykoopman Python - Data-driven Koopman operator approximation. Dynamical system analysis and prediction from time series.
• dynamicslab/pysindy Python - Sparse Identification of Nonlinear Dynamics. Data-driven discovery of governing equations from measurements.
• google-deepmind/graphcast Python - Graph neural network for medium-range weather forecasting. Ten-day forecasts in under a minute (Nature 2023).
• google/jax-cfd Python - JAX-based CFD. Differentiable Navier-Stokes solvers. GPU-accelerated, auto-differentiable.
• Koopman-Laboratory/KoopmanLab Python - Koopman Neural Operator for mesh-free nonlinear PDE solving. Multi-scale decomposition.
• lululxvi/deepxde Python - Deep learning library for PDEs. PINNs, DeepONet. Backends: TensorFlow, PyTorch, JAX, PaddlePaddle.
• microsoft/aurora Python - Foundation model for Earth system prediction. Atmosphere, ocean, air quality. Pre-trained on ERA5 and CMIP6.
• microsoft/ClimaX Python - Foundation model for weather and climate. Pre-trained on CMIP6, fine-tunable for downstream tasks.
• microsoft/mattergen Python - Generative model for novel inorganic materials design. Diffusion-based crystal structure generation with target property conditioning.
• NeuralOperator/neuraloperator Python - Neural operators in PyTorch. FNO, SFNO, UNO for learning PDE solution operators.
• NVIDIA/earth2studio Python - AI-driven Earth system forecasting framework. Built-in model zoo (FourCastNet, Pangu-Weather, CorrDiff, GraphCast).
• NVIDIA/physicsnemo Python CUDA - Physics-ML framework (formerly Modulus). PINNs, neural operators, GNNs, diffusion models. Apache 2.0.
• Terry-cyx/MetaOpenFOAM Python API - LLM-based multi-agent framework for CFD. Automated simulation pipeline from natural language.
• tum-pbs/PhiFlow Python - Differentiable PDE simulations. Fluid dynamics with TF/PyTorch/JAX. ML-physics hybrid workflows.

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Surrogate Models & PINNs

Physics-informed neural networks and data-driven reduced-order models for fast PDE solving.

• camlab-ethz/poseidon Python - Scalable foundation model for PDEs. Pre-trained on diverse physics domains; few-shot generalization via in-context operator learning.
• lululxvi/deepxde Python - Physics-informed neural networks for PDEs. Multi-backend (TF, PyTorch, JAX). Inverse problems, fractional PDEs.
• mathLab/PINA Python - Physics-Informed Neural networks for Advanced modeling. PyTorch Lightning-based with multi-device training.
• mathLab/PyDMD Python - Dynamic Mode Decomposition. Data-driven reduced-order modeling for fluid dynamics and beyond.
• NeuroDiffGym/neurodiffeq Python - Neural network solver for ODEs and PDEs. Flexible architecture with native boundary condition handling.
• NVIDIA/physicsnemo-sym Python - Symbolic AI for physics. Physics-informed neural networks with symbolic equation definition.
• rezaakb/pinns-torch Python PyTorch - Production-ready PINNs in PyTorch. Multi-physics support, inverse problems, uncertainty quantification.
• sciann/sciann Python - Neural networks for scientific computing. Keras-based PINNs with custom loss and constraints.
• thuml/Neural-Solver-Library Python - Library for advanced neural PDE solvers. Benchmarking Transolver, FNO, and variants on diverse PDE families.

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Optimization

Bayesian, topology, and multidisciplinary design optimization.

• meta-pytorch/botorch Python PyTorch - Bayesian optimization in PyTorch. Sequential decision making, multi-objective optimization, batch acquisition.
• OpenMDAO/dymos Python - Open-source optimal control of dynamic systems. Gradient-based trajectory optimization with Gauss-Lobatto and Radau collocation on OpenMDAO.
• OpenMDAO/OpenMDAO Python - Multidisciplinary design optimization. NASA-developed. Gradient-based + surrogate-assisted optimization.
• anyoptimization/pymoo Python - Multi-objective optimization. NSGA-II/III, reference directions, constraint handling, parallelization.
• dl4to/dl4to Python PyTorch - Deep learning for 3D topology optimization. Autograd + adjoint method for efficient neural optimization.
• williamhunter/topy Python - Topology optimization with Python. Minimum compliance, heat conduction, mechanism design.
• mdolab/OpenAeroStruct Python - Aerostructural optimization. VLM aerodynamics + beam FEM structures + ply-level composites.

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Data Formats & I/O

Libraries for reading, writing, and converting simulation data across mesh and field formats.

• nschloe/meshio Python - I/O for mesh formats. Abaqus, CGNS, Gmsh, VTK, XDMF, Exodus, and 30+ more.
• h5py/h5py Python - Pythonic interface to HDF5. Read/write large numerical datasets efficiently.
• Unidata/netcdf4-python Python - Python/NumPy interface to NetCDF. Climate, ocean, atmospheric simulation data.
• CGNS/CGNS C Fortran - CFD General Notation System. Standard for CFD data storage and exchange. HDF5-based.
• pyvista/pyvista Python - Read/write VTK formats (VTI, VTP, VTU, VTS, VTR), STL, OBJ, PLY, glTF, and more.

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Datasets & Benchmarks

Standardized datasets and benchmarks for training and evaluating scientific ML models.

• divelab/AIRS Python - AI for science benchmarks. Molecular, protein, climate, physics datasets.
• Extrality/AirfRANS Python - RANS simulation dataset for airfoils. 1000 simulations with Reynolds-averaged fields (NeurIPS 2022).
• Mohamedelrefaie/DrivAerNet Python - Large-scale automotive CFD dataset. 4000+ car designs with drag coefficients and surface fields.
• i207M/PINNacle Python - Comprehensive PINN benchmark with 20 PDE problems across difficulty levels (NeurIPS 2024).
• NASA TMR - Turbulence Modeling Resource. Validation cases for CFD turbulence models with experimental data.
• pdebench/PDEBench Python - Benchmarks for scientific ML. Standardized PDE datasets with baseline models.
• PolymathicAI/the_well Python - Large-scale collection of diverse physics simulations for ML. Fifteen-plus PDE systems (NeurIPS 2024).

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Learning Resources

Tutorials, courses, and curated reference lists for computational engineering and AI for science.

• barbagroup/CFDPython Python - Classic “12 Steps to Navier-Stokes” tutorial. Learn CFD fundamentals with Python step by step.
• ikespand/awesome-machine-learning-fluid-mechanics - Curated list of ML applications in fluid mechanics. Papers, code, and tutorials.
• jxx123/simglucose Python - Type 1 diabetes simulator. Example of AI-in-the-loop biomedical simulation.
• maziarraissi/PINNs Python - The foundational PINN reference implementation. Data-driven PDE solutions and discovery (JCP 2019).
• thunil/Physics-Based-Deep-Learning - Comprehensive collection of physics-based deep learning resources. Papers, code links, and tutorials from TUM.
• WillDreamer/Awesome-AI4CFD - Survey of ML for CFD covering data-driven surrogates, PINNs, and ML-assisted numerical solvers.

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Star History

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Contributing

Contributions welcome! Read the contributing guidelines first — adding a tool triggers an automatic AI-Readiness score on your PR. Common questions answered in the FAQ.

To the extent possible under law, kimimgo has waived all copyright and related or neighboring rights to this work.