tum-pbs/PhiFlow

Health v2 · maintenance only

Adoption not part of the score

1939 stars · 235 forks observed · 2026-10-01

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PhiFlow is an open-source Python simulation toolkit for solving partial differential equations with support for optimization and machine learning workflows. It integrates with NumPy, PyTorch, JAX, and TensorFlow to provide end-to-end differentiable physics simulations, such as fluid dynamics, combined with neural network models.

Use cases

• run differentiable fluid simulations for machine learning
• train neural networks with physics simulation in the loss loop
• solve PDEs with automatic differentiation
• build physics-based deep learning models
• simulate smoke and fluid dynamics in Python
• optimize simulation parameters via gradients

When to choose

• you need gradients flowing through physics simulations into ML models
• you want a PDE solver that works with PyTorch, JAX, or TensorFlow
• you are doing research in physics-based deep learning or differentiable simulation

When to avoid

• you need a general-purpose CFD tool with production-grade solver accuracy
• you only need non-differentiable simulation without ML integration
• you need simulation domains outside PDE-based physics like rigid body dynamics

Facets

library · maturity active

simulation machine-learning deep-learning math machine-learning simulation deep-learning python cross-platform differentiable-simulation pde-solver fluid-simulation physics-based-deep-learning automatic-differentiation scientific-computing algorithms

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markdown · JSON · MCP: product_card(name="tum-pbs/PhiFlow")