Maxwell Postprocessor: Recovery Theory

Date: 2026-01-20 Purpose: Mathematical foundation and implementation details of field recovery in Maxwell postprocessor Module: src/fem/maxwell

⚠️ AUTHORITATIVE DOCUMENT: This document defines the mathematical model for Maxwell field recovery. Other documents may reference or summarize, but NOT redefine the recovery method.

See Also:

• Maxwell Module README - Overview and quick reference

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Glossary of Electromagnetic Symbols

Symbol	Meaning	Units (SI)	Notes
H	Magnetic field intensity	A/m	Primary variable in h-φ formulation
B	Magnetic flux density	T (Tesla)	B = μ·H
J	Current density	A/m²	J = ∇×H for conductors
Jc	Critical current density	A/m²	Superconductor threshold
φ	Scalar magnetic potential	A (Ampere)	In air/ferro regions
A	Vector magnetic potential	Wb/m	B = ∇×A (not used in h-φ)
μ	Magnetic permeability	H/m	μ = μ₀·μᵣ, nonlinear for ferro/SC
μ₀	Vacuum permeability	4π×10⁻⁷ H/m	Constant
σ	Electrical conductivity	S/m	For Ohmic conductors
T	Temperature	K (Kelvin)	For thermal coupling
σ*	Recovered field	(varies)	SPR-projected quantity
σₕ	Raw FEM field	(varies)	Discontinuous across elements
p(x)	Polynomial basis	-	Monomials for recovery
c	Polynomial coefficients	-	From least-squares fit
V	Gramian/Vandermonde matrix	-	SPD, size m×m

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Overview

The Maxwell postprocessor implements a Superconvergent Patch Recovery (SPR) method to project discontinuous element-wise electromagnetic fields onto smooth nodal values suitable for visualization and output. This document explains the mathematical theory, implementation approach, and domain-specific aspects.

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1. The Recovery Problem

1.1 Why Recovery is Needed

Finite element solutions provide continuous primary variables:

• Lagrange formulation: Scalar potential φ (nodal)
• Nédélec formulation: Edge-based DOFs for H-field

However, derived quantities (gradients, curls, material-dependent fields) are:

• ✅ Accurate at integration points (Gauss quadrature)
• ❌ Discontinuous across element boundaries
• ❌ Unsuitable for direct visualization

Recovery goal: Construct smooth, continuous fields with higher accuracy by exploiting superconvergence properties at integration points.

1.2 Classical Approaches

Method	Description	Accuracy	Cost
Direct averaging	Average element values at nodes	O(h^p)	Very low
L² projection	Global least-squares fit	O(h^(p+1))	High (global solve)
SPR (Zienkiewicz-Zhu)	Local polynomial fit to patches	O(h^(p+1))	Low (local solves)
Gradient recovery (ZZ)	SPR + error estimation	O(h^(p+1))	Medium

BELFEM uses SPR for optimal balance of accuracy and computational cost.

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2. Superconvergent Patch Recovery (SPR)

2.1 Mathematical Formulation

For each node i, recover a smooth field σ* from discontinuous element values σₕ:

Step 1: Define polynomial approximation

σ*(x) = p(x)ᵀ · c

where:

• p(x) = polynomial basis vector (monomials)
• c = unknown coefficients to determine

Step 2: Weighted least-squares on element patch

Minimize the error over all integration points in the patch:

min  Σₖ wₖ · ||σₕ(xₖ) - p(xₖ)ᵀ·c||²

This yields the normal equations:

V · c = b
 
where:
  V = Σₖ wₖ · p(xₖ) · p(xₖ)ᵀ    (Gramian/Vandermonde matrix)
  b = Σₖ wₖ · p(xₖ) · σₕ(xₖ)     (weighted right-hand side)

Step 3: Solve and evaluate

V · c = b           (solve via Cholesky: V is SPD)
σ*(x₀) = p(x₀)ᵀ·c   (evaluate at node location)

2.2 Polynomial Basis Selection

BELFEM uses complete polynomial spaces matching the element interpolation order:

2D Polynomial Bases

Order 1:  p(x,y) = [1, x, y]ᵀ                              (3 terms)
Order 2:  p(x,y) = [1, x, y, x², xy, y²]ᵀ                  (6 terms)
Order 3:  p(x,y) = [1, x, y, x², xy, y², x³, x²y, xy², y³]ᵀ   (10 terms)
Order 4:  p(x,y) = [...up to x⁴, y⁴]ᵀ                      (15 terms)

3D Polynomial Bases

Order 1:  p(x,y,z) = [1, x, y, z]ᵀ                         (4 terms)
Order 2:  p(x,y,z) = [1, x, y, z, x², xy, y², yz, z², zx]ᵀ  (10 terms)
Order 3:  [1, ..., x³, x²y, xy², y³, ...]ᵀ                (20 terms)
Order 4:  [... up to x⁴, y⁴, z⁴]ᵀ                          (35 terms)

Implementation: cl_FEM_Postprocessor.hpp:226-366 (inline functions poly1_2d through poly4_3d)

2.3 Weight Function

BELFEM uses element volume weighting:

wₖ = Vₑ    (volume of element containing integration point k)

This ensures:

• Proper scaling across different element sizes
• Numerical stability of the Gramian matrix
• Consistent units for different field types

Alternative weights considered but not used:

• Inverse distance: w = 1/||x-x₀||² (commented out in code)
• Uniform: w = 1 (less stable)

Code reference: cl_MaxwellPostprocessor.cpp:730, cl_FEM_Postprocessor.cpp:285

2.4 Patch Construction

A patch consists of all elements surrounding a node:

Basic patch (depth=0):

• All elements connected to the node
• Includes duplicates for periodic boundary conditions

Extended patch (depth>0):

• Iteratively adds neighbor elements
• Controlled by mRecoveryDepth parameter
• Improves conditioning but increases cost exponentially

Code reference: cl_MaxwellPostprocessor.cpp:641-691

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3. BELFEM Implementation Details

3.1 Two Recovery Modes

Mode 1: Per-Node Recovery (the SPR pass)

Used by: the base Postprocessor::run(), which MaxwellPostprocessor::run() calls first (cl_MaxwellPostprocessor.cpp:388-390) before its own per-type work. The recovery itself lives in Postprocessor::recover_fields(), not in the Maxwell subclass.

Algorithm:

For each node i:
  1. Build element patch (local neighborhood)
  2. Initialize V = 0, b = 0
  3. For each element e in patch:
       For each integration point k:
         - Compute x_k (physical coordinates)
         - Evaluate p(x_k) (polynomial basis)
         - Compute σ_h(x_k) (field value via compute())
         - Accumulate: V += w·p·pᵀ, b += w·p·σ_h
  4. Solve: posv(V, b)  → coefficients c
  5. Evaluate: σ*(x_i) = p(x_i)ᵀ·c

Advantages:

• Flexible patch sizes (handles irregular meshes)
• Can vary recovery depth per region
• Straightforward to understand

Disadvantages:

• Visits elements multiple times (once per adjacent node)
• Per-node matrix solves (function call overhead)

Code reference: cl_MaxwellPostprocessor.cpp:636-770

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Mode 2: Batch Recovery (recover_fields)

Used by: Parent Postprocessor class (alternative implementation)

Algorithm:

Phase 1: Precompute Gramian matrices (compute_node_matrices, called once)
  For each element e:                          ← first element loop
    For each integration point k:
      - Compute x_k = Nᵀ·coords (physical coordinates)
      - Evaluate p(x_k) (polynomial basis)
      - Compute tA = w·p·pᵀ (outer product)
      - Scatter tA to all owned adjacent nodes
 
Phase 2: Assemble RHS (recover_fields, called every timestep)
  For each element e:                          ← second element loop (redundant geometry work)
    For each integration point k:
      - Compute x_k = Nᵀ·coords (same as Phase 1)
      - Evaluate p(x_k) (same as Phase 1)
      - Compute σ_h(x_k) via compute()
      - Compute tB = w·p·σ_h
      - Scatter tB to all owned adjacent nodes
 
Phase 3: Per-node solve (recover_fields, continued)
  For each node:
    - Copy Gramian: tA = mNodeMatrices(k)      ← copy because posv overwrites
    - Solve: posv(tA, b)                       ← full Cholesky re-factorization every call
    - Evaluate σ*(x_i) = p(x_i)ᵀ·c

Advantages:

• Separates Gramian assembly from RHS assembly
• Can batch node solves

Disadvantages:

• Each element is visited twice (once per phase), with redundant coordinate transforms and polynomial evaluations
• Gramian factorization is repeated every call to recover_fields(), even though the Gramian matrices never change (they depend only on geometry)
• Requires more memory (store all node Gramian matrices)
• Less flexible for varying patch depths

Code reference: cl_FEM_Postprocessor.cpp:219-487

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Mode 3: Gradient Recovery (Gradient class)

Used by: cl_Gradient.cpp (independent implementation)

Algorithm:

For each node i:
  1. Reset V = 0, C = 0
  2. For each element e connected to node i (including duplicates):
       For each integration point k:
         - Compute x_k (physical coordinates)
         - Compute J = dNdXi · X (Jacobian)
         - Compute gradient: g = J⁻¹ · dNdXi · φ_e
         - Evaluate p(x_k) (polynomial basis)
         - Accumulate: V += p·pᵀ, C += p·gᵀ
  3. Solve: gesv(V, C, pivot)
  4. Evaluate: grad*(x_i) = p(x_i)ᵀ · C

Note: Uses gesv (general LU) instead of posv (Cholesky), despite V being SPD. This is functionally correct but suboptimal.

Disadvantages:

• Per-node: visits each element once per adjacent node (same redundancy as Mode 1)
• Rebuilds Vandermonde matrix from scratch every call
• Allocates mPhi and mElX per element via set_size() inside the loop

Code reference: cl_Gradient.cpp:100-226

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3.2 Numerical Properties

Gramian Matrix V:

• Type: Symmetric positive definite (SPD)
• Size: m × m, where m = number of polynomial coefficients (3-35)
• Condition number: Typically well-conditioned for volume-weighted patches
• Solver: Cholesky factorization via LAPACK posv (optimal for SPD)

Failure modes:

• Singular V: patch too small or all integration points collinear
• Ill-conditioned V: highly stretched elements or large aspect ratios
• Recovery: code does not explicitly check; posv may fail silently

Potential improvement: Add condition number check or SVD fallback.

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4. Maxwell-Specific Field Recovery

4.1 Field Computation at Integration Points

The compute() function computes physical quantities from primary DOFs:

Domain Type	Primary DOFs	Field Computation	Code Reference
Air	φ (nodal)	H = -∇φ = -B(ξ)·φ	compute_air:909-917
Ferro	φ (nodal)	H = -∇φ B = μ(H)·H	compute_ferro:919-932
Conductor	edge_h (Nédélec)	H = E(ξ)·edge_h B = μ(H)·H J = C(ξ)·edge_h = ∇×H	compute_conductor:934-949
Superconductor	edge_h (Nédélec)	H, B, J (as above) J/Jc = J / Jc(B,T,θ)	compute_superconductor:1008-1060
Thin-shell SC	edge_h + φₘ, φₛ	H, B, J, J/Jc (includes normal field from master/slave)	compute_superconductor_ts:1063-1148

Key operations:

• B(ξ) = gradient operator in reference coordinates (Lagrange elements)
• E(ξ) = Nédélec edge interpolation (H(curl) space)
• C(ξ) = Nédélec curl operator
• μ(H) = nonlinear permeability from material law

4.1.1 Linearity Analysis of Field Computations

For precomputed weight optimization, it is critical to know which fields are linear in the DOFs (can be expressed as M · DOFs) and which are nonlinear:

Domain	Field	Computation	Linear in DOFs?
Air	H	-B(ξ) · φ	Yes
Ferro	H	-B(ξ) · φ	Yes
Ferro	B	μ(‖H‖) · H	No (μ depends on ‖H‖)
Conductor	H	E(ξ) · edge_h	Yes
Conductor	B	μ(‖H‖) · H	No
Conductor	J	C(ξ) · edge_h	Yes
Superconductor	H	E(ξ) · edge_h	Yes
Superconductor	B	μ(‖H‖) · H	No
Superconductor	J	C(ξ) · edge_h	Yes
Superconductor	J/Jc	J / Jc(‖B‖, θ, T)	No

Consequence: H and J can be recovered using precomputed element-wise weight matrices (see Section 13). B and J/Jc require integration-point-level evaluation due to material nonlinearity.

4.2 Material Nonlinearity

For ferromagnetic and superconducting materials:

B = μ(H, T, x) · H

The permeability is evaluated at each integration point:

• Ferro: μ = μ(||H||) from B-H curve
• Superconductor: μ = μ₀ (linear), but Jc = Jc(||B||, θ, T, x)

Thermal coupling: If thermal kernel is active, temperature T is interpolated from thermal DOFs at the same integration point.

Code reference: cl_MaxwellPostprocessor.cpp — the mMaterial->mu( norm( mH ) ) evaluation sites (search mMaterial->mu)

4.3 Thin-Shell Specifics

For thin-shell superconductors, the recovery includes:

Normal field contribution:

bn = -0.5·μ₀·(Bm(ξ)·φₘ + Bs(ξ)·φₛ)

where φₘ, φₛ are potentials from master/slave elements.

Total field:

b = B_tangential + bn · n

Angle for critical current:

β = acos(n · b / ||b||)    (angle between normal and total field)
Jc = Jc(||b||, β, T)

This accounts for the angular dependence of critical current in anisotropic superconductors.

Code reference: cl_MaxwellPostprocessor.cpp — the mJJc /= mMaterial->jc(...) normalization block (search mJJc)

4.4 T-Matrices: Constraint Matrices for Hanging Nodes

What are T-Matrices?

T-matrices (transformation/constraint matrices) enforce continuity of tangential H-field at mesh refinement interfaces where hanging nodes or hanging facets exist. In h-refined meshes (adaptive refinement), constrained DOFs on fine-mesh boundaries must satisfy compatibility with coarse-mesh DOFs.

Why are they needed?

Nédélec edge elements naturally ensure tangential continuity of H within conforming meshes. However, at hanging-node interfaces (where fine elements meet coarse elements):

Coarse element edge:  [--------]
                         |  |
Fine element edges:   [--][--]   ← Hanging node in middle

Without constraints, the two fine-element edge DOFs are independent, potentially creating discontinuity in tangential H at the interface. This violates Maxwell's equations:

n × (H₁ - H₂) = 0    (tangential H must be continuous across interfaces)

T-matrices enforce this constraint by expressing fine-mesh DOFs as linear combinations of coarse-mesh DOFs.

Mathematical Formulation:

For constrained DOFs (hanging), express them in terms of independent DOFs:

h_constrained = T · h_independent
 
where:
  T = sparse constraint matrix (m_constrained × n_independent)
  h_constrained = dependent edge DOFs
  h_independent = master edge DOFs

System-level projection:

When assembling stiffness matrix K and RHS f, constrained DOFs are eliminated via:

K_reduced = Tᵀ · K · T
f_reduced = Tᵀ · f

This reduces the system size and ensures constraints are satisfied automatically.

Maxwell-Specific Implementation:

BELFEM's maxwell::TMatrix class (cl_Maxwell_TMatrix.hpp/cpp) computes constraint coefficients for thin-shell interfaces where master and slave elements meet:

Key operations:

1. Facet linking: Link TRI6 facet (on TET10 master/slave elements)

   mCalc->link(aFacet);  // cl_Maxwell_TMatrix.cpp:46

2. Gradient computation: Compute nabla operator (inverse Jacobian)

   compute_nabla(aIndex);  // cl_Maxwell_TMatrix.cpp:116-138
   mNabla = J⁻¹  (3×4 matrix for tetrahedral reference element)

3. Nédélec edge functions: Evaluate two Nédélec basis functions on facet

   compute_nedelec_function(i, j, k, aIndex);  // cl_Maxwell_TMatrix.cpp:141-175
    
   E₁ = (16·λⱼ·λₖ)·∇λᵢ + (-8·λᵢ·λₖ)·∇λⱼ + (-8·λᵢ·λⱼ)·∇λₖ
   E₂ = (-8·λⱼ·λₖ)·∇λᵢ + (16·λᵢ·λₖ)·∇λⱼ + (-8·λᵢ·λⱼ)·∇λₖ

   where λᵢ, λⱼ, λₖ are barycentric coordinates.
4. Integration over facet: Compute constraint coefficients via Gauss quadrature

   // For each integration point p on facet:
   T(i,j) -= w(p) · (E × n) · ∇φⱼ · dS(p)  // cl_Maxwell_TMatrix.cpp:86-94
    
   where:
     E = Nédélec edge function
     n = outward normal vector
     ∇φⱼ = gradient of Lagrange basis on slave element
     w(p) = integration weight
     dS(p) = surface Jacobian

5. Return constraint vector: Returns 12-element vector (2 edge functions × 6 facet edges)

   process(mesh::Facet*) → Vector<real>(12)  // cl_Maxwell_TMatrix.cpp:43-112

Parent Class: General T-Matrix Storage

The parent class fem::Tmatrix (cl_FEM_Tmatrix.hpp/cpp) stores constraint matrices in CSR (Compressed Sparse Row) format:

// Stored data:
uint* mPointers;  // Row pointers (CSR format)
uint* mIndices;   // Column indices
real* mValues;    // Non-zero constraint coefficients
 
// Key operations:
project(Vector<real>& A, Vector<real>& B);  // B = T·A
project(Matrix<real>& A, Matrix<real>& B);  // B = Tᵀ·A·T

Usage in Recovery:

During postprocessing, T-matrices ensure recovered fields respect hanging-node constraints:

1. DOF retrieval: When update_dofs() gathers edge DOFs, constrained DOFs are automatically computed from master DOFs
2. Field evaluation: Nédélec interpolation (E(ξ)·edge_h) uses constrained DOF values
3. Continuity: Recovered H, B, J fields are automatically continuous across refinement interfaces

When are T-Matrices Created?

T-matrices are precomputed during mesh initialization if:

• ✅ Mesh has hanging nodes (from h-adaptive refinement)
• ✅ Nédélec elements are used (edge-based DOFs)
• ✅ HDF5 file contains T-matrix data (read_node_tmatrix_from_hdf5)

For uniform meshes without hanging nodes, T-matrices are not needed.

Debugging and Validation:

The Maxwell TMatrix implementation includes assertion checks (cl_Maxwell_TMatrix.cpp:70-79):

// Verify master and slave integration points coincide geometrically:
BELFEM_ASSERT(||P_facet - P_master|| < 1e-10, "Master point mismatch");
BELFEM_ASSERT(||P_facet - P_slave|| < 1e-10, "Slave point mismatch");

Code References:

File	Purpose	Key Lines
cl_Maxwell_TMatrix.hpp	Maxwell-specific header	23-54
cl_Maxwell_TMatrix.cpp	Constraint computation	43-175
cl_FEM_Tmatrix.hpp	General CSR storage	22-82
cl_FEM_Tmatrix.cpp	T·v and Tᵀ·A·T operations	(implementation)
hangingnodes.cpp	Example usage	25, 46

Literature Reference:

The mathematical foundation for thin-shell interface constraints and cohomology-based cut generation is covered in:

• Alves et al. 2022b, Section 3: "Cohomology basis computation and cut generation"
• Messe et al. 2023, Section 2.4: "Interface coupling via static condensation"

The general theory of hanging-node constraints in FEM is covered in:

• Zienkiewicz & Taylor, Vol. 1, §10.5: "Hierarchical and h-adaptive meshes"
• Brenner & Scott (2008), The Mathematical Theory of Finite Element Methods, Ch. 4: "Constraint elimination"

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5. Theoretical Foundation: Literature References

5.1 Primary References

Superconvergent Patch Recovery:

1. Zienkiewicz, O.C. & Zhu, J.Z. (1992) "The superconvergent patch recovery and a posteriori error estimates. Part 1: The recovery technique" International Journal for Numerical Methods in Engineering, 33(7), 1331-1364 DOI: 10.1002/nme.1620330702
2. Zienkiewicz, O.C. & Zhu, J.Z. (1992) "The superconvergent patch recovery and a posteriori error estimates. Part 2: Error estimates and adaptivity" International Journal for Numerical Methods in Engineering, 33(7), 1365-1382 DOI: 10.1002/nme.1620330703
3. Zienkiewicz, O.C. & Zhu, J.Z. (1987) "A simple error estimator and adaptive procedure for practical engineering analysis" International Journal for Numerical Methods in Engineering, 24(2), 337-357 DOI: 10.1002/nme.1620240206

Textbook treatment:

1. Zienkiewicz & Taylor (2000) The Finite Element Method, Vol. 1, 5th ed. Chapter 15: "Errors and Recovery" (pp. 493-544) Sections: §15.2 (Superconvergence), §15.4 (SPR), §15.6 (Error estimates)

5.2 Related FEM Theory

Superconvergence theory:

1. Babuška, I. & Strouboulis, T. (2001) The Finite Element Method and its Reliability Oxford University Press, Chapter 9

Moving Least Squares (MLS) connection:

1. Lancaster, P. & Salkauskas, K. (1981) "Surfaces generated by moving least squares methods" Mathematics of Computation, 37(155), 141-158

5.3 Maxwell-Specific References

Nédélec element postprocessing:

1. Monk, P. (2003) Finite Element Methods for Maxwell's Equations Oxford University Press, Chapters 5-6
2. Bossavit, A. (1998) Computational Electromagnetism: Variational Formulations, Complementarity, Edge Elements Academic Press

BELFEM implementation papers:

1. Messe et al. (messe2023.txt) - BELFEM core architecture and h-φ formulation
2. Arsenault et al. (arsenault2023.txt) - Magnetodynamic h-φ coupling and postprocessing

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6. Superconvergence Properties

6.1 Why Integration Points are Special

Theorem (Herrmann, 1978): For polynomial finite elements of degree p, derivatives (gradients, curls) are superconvergent at Gauss integration points:

||σ_exact - σ_h||_∞ = O(h^(p+1))    at Gauss points
||σ_exact - σ_h||_∞ = O(h^p)        elsewhere

Consequence: Sampling at integration points and fitting a polynomial of degree p recovers one extra order of accuracy.

Reference: Zienkiewicz Vol. 1, §15.2

6.2 Recovery Convergence Rate

Standard FEM:

||u - u_h|| = O(h^(p+1))           (solution)
||∇u - ∇u_h|| = O(h^p)             (gradient/curl)

With SPR:

||∇u - ∇u_h*|| = O(h^(p+1))        (recovered gradient/curl)

Practical implication: For linear elements (p=1), gradients improve from O(h) to O(h²).

6.3 Conditions for Superconvergence

SPR achieves optimal convergence when:

1. ✅ Mesh is sufficiently regular (not highly distorted)
2. ✅ Polynomial degree matches element order
3. ✅ Patch contains enough sampling points (at least m = number of coefficients)
4. ✅ Weights are properly scaled (volume-weighting ensures this)

BELFEM satisfies all conditions for typical electromagnetics meshes.

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7. Comparison: Recovery vs. Filter

BELFEM's postprocessor applies two smoothing mechanisms internally (there are no user-callable process_*() entry points — the class interface is run() / initialize(), driven by the controller at every saved timestep):

Property	Patch recovery (SPR)	Inverse-distance filtering
Method	Polynomial least-squares (SPR)	Inverse-distance weighted average
Accuracy	O(h^(p+1))	O(h^p)
Cost	Medium (small matrix solve)	Low (simple averaging)
Smoothness	C⁰ continuous, polynomial	C⁰ continuous, piecewise
Use case	Production, accurate visualization	Quick/debug visualization
Code	cl_MaxwellPostprocessor.cpp (search patch recovery)	cl_MaxwellPostprocessor.cpp

Filter formula:

σ*(x₀) = Σₖ wₖ·σₕ(xₖ) / Σₖ wₖ
where wₖ = 1/||x_k - x₀||²

This is Shepard's method (1968), a simple form of Moving Least Squares (MLS) with inverse-distance weighting.

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8. MPI Parallelization Strategy

8.1 Domain Decomposition

Each MPI rank processes:

• Owned nodes: Nodes where node->owner() == mCommRank
• Aura elements: Neighboring elements needed to build patches

Patch assembly:

• Local (no communication required)
• Each rank independently builds V and b for its owned nodes

8.2 Communication Pattern

Phase 1: Node distribution (collect_nodes)

• Rank 0 identifies which nodes belong to each rank
• Broadcasts node IDs to all ranks
• Each rank creates local mNodes list

Phase 2: Field computation (run)

• Parallel (no communication)
• Each rank recovers fields for owned nodes

Phase 3: Global assembly (collect_fields)

• All ranks send their nodal field values to rank 0
• Rank 0 assembles into global field vectors
• Uses blocking send/collect (could be optimized)

Code reference: cl_MaxwellPostprocessor.cpp:426-500, 374-386

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9. Error Estimation Extension (Future)

The Zienkiewicz-Zhu (ZZ) error estimator extends SPR:

Estimate local error:

η_e = ||σ_h - σ*||_{L²(Ω_e)}    (element-wise)

Global error estimate:

||∇u - ∇u_h|| ≈ (Σ_e η_e²)^(1/2)

This is used for adaptive mesh refinement (h-adaptivity).

Implementation status in BELFEM: Not currently implemented, but infrastructure is in place:

• Recovered fields are already computed
• Element-wise fields are available (compute_element_data)
• Missing: norm computation and refinement logic

Reference: Zienkiewicz Vol. 1, §15.6

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10. Implementation Clarification: SPR vs L² Projection

Terminology note: The BELFEM implementation is true SPR (Zienkiewicz-Zhu), not simple L² projection.

What BELFEM Does (SPR):

1. Sample fields at superconvergent Gauss integration points
2. Fit polynomial to patch samples via weighted least-squares
3. Solve local SPD system: V·c = b (Cholesky)
4. Extrapolate polynomial to node location

What BELFEM Does NOT Do:

❌ Global L² projection: ∫ N·σ* dΩ = ∫ N·σ_h dΩ (would require global solve)
❌ Simple nodal averaging: σ* = average of adjacent element values
❌ Moving least squares (MLS) with distance weighting (except in filter mode)

Code Evidence:

• Patch construction: cl_MaxwellPostprocessor.cpp:641-691
• Gauss-point sampling: cl_MaxwellPostprocessor.cpp:713-751
• Local solve: cl_MaxwellPostprocessor.cpp:755 (posv(mVandermonde, mCoefficients))
• Polynomial extrapolation: cl_MaxwellPostprocessor.cpp:757-768

This IS the classic SPR algorithm from Zienkiewicz & Zhu (1992).

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11. What BELFEM Recovery Deliberately Does NOT Do

By design, the following are NOT implemented:

1. Global L² Projection

• ❌ No global mass matrix assembly
• ❌ No global linear solve for recovery
• Why: Local SPR is faster and scales better to large meshes

2. Feedback into Solver

• ❌ Recovered fields do NOT influence next solve step
• ❌ No error-driven adaptive remeshing (yet)
• Why: Recovery is postprocessing only; avoids coupling issues

3. Alternative Recovery Methods

• ❌ No gradient averaging (simple nodal averaging)
• ❌ No equilibrated recovery (REP method)
• ❌ No moving least squares (except filter mode)
• Why: SPR provides optimal accuracy/cost tradeoff for BELFEM use cases

4. Stability Enhancements

• ❌ No SVD fallback for ill-conditioned patches
• ❌ No adaptive polynomial order reduction
• ❌ No condition number checking
• Why: Volume-weighting typically ensures well-conditioned systems

5. Advanced Features (Not Yet Implemented)

• ❌ No ZZ error estimator (infrastructure exists, not active)
• ❌ No higher-order recovery (p+2, p+3 polynomials)
• ❌ No recovery on curved manifolds
• Why: Current implementation satisfies production needs

Recovery depth > 0: Use only if Gramian is ill-conditioned at depth 0 (rare). Increases cost exponentially.

If you need these features, consult development team before implementing.

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12. Summary

The Maxwell postprocessor implements a mathematically rigorous Zienkiewicz-Zhu Superconvergent Patch Recovery:

✅ Exploits superconvergence at Gauss integration points ✅ Polynomial fitting via weighted least-squares ✅ Volume-weighted Gramian ensures stability ✅ Cholesky solver for SPD systems (optimal) ✅ Domain-aware field computation (H, B, J, Jc) ✅ MPI-parallel with local patch assembly

Convergence rate: O(h^(p+1)) for recovered gradients/curls (one order better than raw FEM)

Key references:

• Theory: Zienkiewicz & Zhu (1992), Int. J. Numer. Methods Eng., 33(7)
• Textbook: Zienkiewicz & Taylor, Vol. 1, Chapter 15
• Implementation: cl_FEM_Postprocessor.cpp, cl_MaxwellPostprocessor.cpp

For potential GPU acceleration discussion (extreme-scale only), see: ../../../../todo/maxwell_postprocessor_gpu_acceleration.md

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13. Planned Optimization: Element-Wise Precomputed Weight Matrices

Status: Planned. See todo/postprocessor_recovery_findings.md for full analysis, memory estimates, cache invalidation rules, and validation gates.

13.1 Motivation

All three current recovery modes (Sections 3.1) share the same fundamental inefficiency: geometry-dependent work (Vandermonde assembly, Cholesky factorization, coordinate transforms, polynomial evaluation) is repeated every timestep, even though it depends only on static mesh geometry.

Additionally, per-node recovery (Modes 1 and 3) visits each element multiple times — once per adjacent node — multiplying the element loop cost by ~6 in 2D.

13.2 Key Insight

The SPR result at node n can be decomposed as:

z_n = p(x_n)ᵀ · V_n⁻¹ · b_n                                              (*)

Let q_n = V_n⁻¹ · p(x_n) (computed via Cholesky factor/solve, not explicit inversion). Since q_n depends only on geometry, it is precomputable.

For linear fields (H, J), where y_{e,ip} = M_e(x_ip) · DOFs_e:

z_n = Σ_e  [Σ_ip  w_e · dot(q_n, p(x_{e,ip})) · M_e(x_ip)] · DOFs_e
    = Σ_e  W_e[n] · DOFs_e

where W_e[n] is a small precomputed matrix per element per node:

W_e[n] = Σ_ip  w_e · dot(q_n, p(x_{e,ip})) · M_e(x_ip)

13.3 Element-Wise Storage

Store one matrix W_e per element of size (n_nodes × n_fields) × n_dofs:

// Precomputed during initialize() — one-time cost
Cell< Matrix< real > > mWeightMatrices ;  // one W_e per element
 
// Runtime — single element loop, each element visited exactly once
for each element e:
    gather DOFs_e
    z_local = W_e * DOFs_e           // small matrix-vector product
    scatter-add z_local to node fields

Advantages over current implementation:

• Each element visited exactly once (vs ~6x in per-node modes)
• Zero runtime linear algebra (no Vandermonde, no Cholesky, no poly eval)
• Zero index overhead (DOFs and nodes implicit from element topology)
• Cache-friendly contiguous matrix per element
• Natural FEM scatter-add pattern (identical to stiffness assembly)

13.4 Nonlinear Fields

For nonlinear fields (B, J/Jc), DOF-based weights cannot be used because the field depends nonlinearly on DOFs (see Section 4.1.1).

Chosen approach: Use precomputed scalar alpha weights at the integration-point level for nonlinear fields, preserving numerical equivalence with the current method. See todo/postprocessor_recovery_findings.md for details and acceptance criteria.

13.5 Applicability

Recovery class	Current mode	Optimization applies?
MaxwellPostprocessor	Per-node (Mode 1)	Yes — highest impact
Postprocessor base	Batch (Mode 2)	Yes
Gradient	Per-node (Mode 3)	Yes (M = J⁻¹ · dNdξ)

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Revision History

Version	Date	Author	Changes
1.3	2026-03-06	C. Messe	Fixed Mode 2 description (was overstating advantages). Added Mode 3 (Gradient). Added Section 4.1.1 (linearity analysis). Added Section 13 (element-wise W_e optimization plan).
1.2	2026-01-20	C. Messe	Added Section 4.4: T-Matrix explanation (constraint matrices for hanging nodes)
1.1	2026-01-20	C. Messe	Added cross-references, glossary, SPR clarification, "What NOT to do" section
1.0	2026-01-20	C. Messe (via Claude Code)	Initial comprehensive documentation