perifit 2.0.1

Surface correction weights for 3-D peridynamics (bond-based, state-based LPS, and correspondence NOSB-PD) with mesh quality diagnostics

perifit

Surface correction weights for 3-D peridynamics (state-based LPS, bond-based, and correspondence NOSB-PD) with mesh quality diagnostics

perifit computes optimised nodal influence weights that eliminate the surface/boundary effect in three-dimensional peridynamics.

Supported models:

Model	CLI flag	Operator rows	Notes
OSB-PD (ordinary state-based, LPS)	--model osb	109	Poisson-unrestricted
BB-PD (bond-based)	--model bb	54	ν = 1/4 in 3D
NOSB-PD (non-ordinary, correspondence)	--model nosb	36	Material-independent

Provide any 3-D mesh → get per-node weights in formats ready for Peridigm, PeriLab, or generic CSV / DAT / VTK.

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Background

In peridynamics, nodes near the domain boundary have truncated horizon neighbourhoods. This reduces their effective stiffness and causes displacement errors that do not vanish under mesh refinement at fixed horizon-to-spacing ratio m = δ/Δx — the so-called surface effect.

perifit implements an operator-matching framework for state-based (LPS), bond-based (solid), and correspondence (NOSB-PD) models. For each node, a local overdetermined least-squares problem is solved to find a scalar influence weight wᵢ such that the truncated neighbourhood operators reproduce their full-sphere continuum counterparts. The per-node weights are coupled through a global sparse linear system solved by BiCGSTAB.

The weights are purely geometric. Material constants (G for state-based, c for bond-based) appear as common prefactors in both the local ML matrix and the analytical targets and cancel exactly after per-row normalisation. For the correspondence model, the operator rows are material-independent by construction (no radial singularity, no constitutive constants). Consequently, E, ν, and any other material constant are not required — only the mesh geometry and the horizon matter.

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Results

Convergence and preprocessing time (state-based LPS)

(a) Displacement error convergence for the state-based LPS model on a unit bar under uniaxial tension (m = 3). The corrected weights (solid lines) recover first-order convergence across all Poisson ratios, while standard LPS (dashed) stagnates due to the surface effect. (b) Weight computation time scales near-linearly with the number of nodes.

Mesh quality diagnostics (state-based LPS)

(a) Rocker-arm geometry (1,699 nodes) colored by LPS weight values. (b) Outlier nodes flagged by the built-in diagnostics (2σ threshold). (c) Cleaned mesh after removing the 69 flagged nodes — weight distribution is now uniform and well-conditioned.

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Installation

pip install perifit

With optional dependencies for specific mesh formats:

pip install "perifit[hdf5]"     # PeriLab HDF5 output
pip install "perifit[exodus]"   # Exodus mesh input
pip install "perifit[gmsh]"     # Gmsh / Abaqus / VTU input
pip install "perifit[all]"      # everything

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Quick start

Built-in demo (no mesh needed)

perifit --demo

Runs on the bundled 1000-node structured cube mesh and writes csv, dat, and vtk output to ./perifit_output/.

Python API

import numpy as np
from perifit import compute_weights, write_weights

# Build your mesh (or load it — see below)
dx = 0.05
xs = np.arange(dx / 2, 1.0, dx)
gx, gy, gz = np.meshgrid(xs, xs, xs, indexing='ij')
coords = np.column_stack([gx.ravel(), gy.ravel(), gz.ravel()])

# OSB-PD / LPS (default model is "bb"):
volumes = np.full(len(coords), dx**3)
weights = compute_weights(coords, volumes, horizon=3 * dx, model='osb')

# BB-PD:
weights = compute_weights(coords, volumes, horizon=3 * dx, model='bb')

# NOSB-PD (material-independent):
weights = compute_weights(coords, volumes, horizon=3 * dx, model='nosb')

# Export to any format
write_weights(
    coords, volumes, weights,
    outdir='./output',
    formats=['csv', 'vtk', 'peridigm', 'perilab'],
)

Load from a mesh file

from perifit import load_mesh, compute_weights, write_weights

# Supported: .csv .dat .txt .npy .npz .vtk .vtu .exo .g .msh .inp
coords, volumes = load_mesh('my_mesh.exo')

weights = compute_weights(coords, volumes, horizon=3 * dx, model='osb')

write_weights(coords, volumes, weights,
              outdir='./weights',
              formats=['peridigm', 'perilab', 'csv'])

Mesh quality diagnostics

from perifit import compute_weights
from perifit.quality import diagnose_weights, clean_mesh

weights = compute_weights(coords, volumes, horizon=delta)

# Diagnose: flag negative weights and statistical outliers
diag = diagnose_weights(weights, threshold_sigma=2.0)
print(diag.summary)

# Clean: remove flagged nodes
result = clean_mesh(coords, volumes, weights, threshold_sigma=2.0)
print(f"Removed {result.original_n - result.cleaned_n} nodes")

Command-line interface

# OSB-PD / LPS
perifit --mesh nodes.csv --model osb

# BB-PD (default)
perifit --mesh nodes.csv --model bb

# NOSB-PD (material-independent weights)
perifit --mesh nodes.csv --model nosb

# Specify horizon-to-spacing ratio (default is 3)
perifit --mesh nodes.csv --m-ratio 4 --output csv,vtk

# Provide the horizon explicitly instead
perifit --mesh solid.exo --horizon 0.005 --output peridigm

# Diagnose mesh quality after computing weights
perifit --mesh cube.csv --diagnose

# Clean mesh (remove negative/outlier weight nodes)
perifit --mesh cube.csv --clean --threshold-sigma 2.0

# Show all options
perifit --help

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Supported input formats

Extension	Format	Notes
.csv, .txt, .dat	Plain text	Columns: x, y, z[, volume]
.npy	NumPy array	Shape (N, 3) or (N, 4)
.npz	NumPy archive	Keys: coords[, volumes]
.vtk	Legacy VTK ASCII	Unstructured or structured
.vtu	VTK XML	Requires pyvista or meshio
.exo, .g	Exodus II	Requires netCDF4 or meshio
.msh	Gmsh v2/v4	Requires meshio
.inp	Abaqus	Requires meshio

Supported output formats

Format	Description
csv	node_id, x, y, z, volume, weight
dat	Space-delimited text
vtk	Legacy ASCII VTK for ParaView / VisIt
peridigm	Text mesh + weight CSV + YAML input snippet
perilab	HDF5 mesh with Nodal_Surface_Correction field + YAML snippet

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Applying the weights in your solver

The bond-averaged influence weight is

w̄ᵢⱼ = (wᵢ + wⱼ) / 2

Replace every occurrence of the unit influence function ω(ξ) = 1 in your PD discretisation with w̄ᵢⱼ. The Peridigm and PeriLab output files include a YAML template showing exactly where to hook in the field.

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Auto-inference of dx, volumes, and horizon

The nodal spacing dx is always estimated automatically as the median nearest-neighbour distance (KD-tree, O(N log N)). This is used to:

1. Set nodal volumes to dx³ when not provided.
2. Compute the horizon as delta = m_ratio × dx when --horizon is not given.
3. Check consistency when both --horizon and --m-ratio are supplied.

Input modes

--horizon	--m-ratio	Behaviour
not given	not given	horizon = 3 × dx (default m=3)
not given	given	horizon = m_ratio × dx
given	not given	horizon used directly
given	given	horizon used; warning if inconsistent with m_ratio × dx

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License

MIT — see LICENSE.