Neural Operator Mixing: Hybrid preconditioner based on a mathematical preconditioner and a preconditioner learned by machine learning.
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Updated
Jan 25, 2026 - Jupyter Notebook
Neural Operator Mixing: Hybrid preconditioner based on a mathematical preconditioner and a preconditioner learned by machine learning.
Demo project for teaching purposes
Demo and simple scripts using Firedrake
viskex - interactive visualization for firedrake and FEniCSx
Mesh movement methods for finite element problems solved using Firedrake
Mesh adaptation utilities for coastal ocean modelling in Firedrake and Thetis.
Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake
Seismic inversion using a neural network regulariser implemented as an ExternalOperator in Firedrake
The firedrake-ts library provides an interface to PETSc TS for the scalable solution of DAEs arising from the discretization of time-dependent PDEs.
Automatic differentiation of FEniCS and Firedrake models in Julia
Differentiable interface to Firedrake for JAX
Easy interoperability with Automatic Differentiation libraries through NumPy interface to Firedrake and FEniCS
Physics-driven machine learning using PyTorch and Firedrake
[NeurIPS 2024 Spotlight] Towards Universal Mesh Movement Networks
FEMlium – interactive visualization of finite element simulations on geographic maps with folium
Slides/notes and Jupyter notebook demos for an introductory course of numerical methods for PDEs
Finite Element Method for Electrochemical Transport (EchemFEM)
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