User Guide¶
In-depth, topic-oriented guides covering TensorMesh’s core concepts, design choices, and how to wield each component.
What TensorMesh is, the FEM pipeline, and how the modules fit together.
Build, inspect, and load/save meshes; per-node and per-cell data.
The element zoo, basis functions, quadrature rules, and ordering conventions.
Write weak forms in pure Python via the three assembler base classes.
Apply Dirichlet BCs via static condensation; handle Neumann naturally.
Linear and nonlinear sparse solves via torch-sla — five backends, batched RHS, Newton / Picard / Anderson.
Explicit and implicit-linear Runge-Kutta schemes for transient problems.
Three axes of batching: memory chunking, batched RHS, and ML datasets.
End-to-end gradients through assemble → solve, for inverse problems and topology opt.