Category FiPy: Documentation, Examples & Development

Reading Time: 7 minutesOpen-source development is one of the best ways to learn how real software is built. It gives developers a chance to read production code, understand project structure, work with issues, write tests, improve documentation, and communicate with maintainers. For scientific software, open-source contribution is especially valuable. These projects do not only need clean code. They […]

Reading Time: 8 minutesScientific code often begins as a quick script. A researcher needs to clean a dataset, run a simulation, test a model, generate a figure, or check a hypothesis. At first, the code may be written for one person and one immediate task. But over time, that same script can become part of a published paper, […]

Reading Time: 7 minutesFiPy is often used in research workflows where small software changes can matter. A new release may include documentation updates, dependency adjustments, solver-related fixes, examples, performance notes, or changes that affect how a simulation is configured. For casual experimentation, these updates may seem minor. For published research, teaching material, or long-running modeling projects, they can […]

Reading Time: 8 minutesFiPy is most useful when you stop treating it as a collection of isolated example scripts and start using it as a flexible framework for building your own PDE workflows. That shift matters because real simulation projects rarely stay simple for long. A model that begins as one equation on one mesh often grows into […]

Reading Time: 11 minutesFiPy simulations can achieve 10x to 100x speedups by moving compute-intensive operations to the GPU using CuPy (drop-in NumPy replacement) or Numba (JIT compilation). CuPy excels at array operations and requires minimal code changes, while Numba accelerates Python loops and custom functions. However, GPU acceleration isn’t always beneficial—small problems, memory-bound operations, and complex data structures […]

Reading Time: 10 minutesFiPy does not have built-in adaptive mesh refinement (AMR). Current approaches involve external mesh generation with Gmsh (inefficient for dynamic problems), integration with dedicated AMR libraries like libmesh (architecturally challenging), or switching to alternative phase-field codes that support AMR natively (MOOSE, PRISMS-PF). AMR provides significant speedups (often 2–10×) for phase-field problems with localized interfaces, but […]

Reading Time: 9 minutesTL;DR Multi-physics simulations often require coupling multiple specialized solvers. preCICE is a mature, open-source coupling library that enables partitioned multi-physics simulations in Python. This tutorial shows how to couple FiPy with another solver using preCICE, covering installation, adapter implementation, configuration, and common pitfalls. You’ll learn when to use partitioned coupling, how to set up data […]

Reading Time: 4 minutesScientific computing tools are powerful but often intimidating, especially for students and researchers encountering them for the first time. One such tool is FiPy, a Python-based library designed for solving partial differential equations (PDEs) using the finite volume method. FiPy is widely used in computational materials science, fluid dynamics, electrochemistry, and phase-field modeling. However, many […]

Reading Time: 4 minutesPartial Differential Equations (PDEs) lie at the heart of scientific computing. They describe heat diffusion, fluid flow, structural deformation, electromagnetic fields, chemical reactions, and climate dynamics. As computational power has increased, so has the ambition of simulation-based science. Researchers now routinely solve PDE systems with millions or billions of unknowns, coupling multiple physical processes across […]

Reading Time: 3 minutesSimulation models can generate vast amounts of data. Differential equations produce time series with thousands of points. Finite element models output multidimensional spatial fields. Monte Carlo simulations yield distributions across thousands of runs. Without effective visualization, these results remain opaque and difficult to interpret. Visualization is not an afterthought—it is part of the modeling process. […]