PAN Model: Multidomain Review

• PAN Model is a suite of diverse frameworks spanning turbulence simulation, computer vision, segmentation, differential privacy, microbial ecology, and statistical imputation.
• In turbulence, the PANS approach bridges RANS and LES using tunable filter parameters for efficient resolution control and accurate energy partitioning.
• Other variants leverage joint attribute weighting, projective adversaries, and Bayesian data augmentation to enhance similarity predictions, privacy guarantees, gene diversity, and imputation consistency.

The term "PAN Model" encompasses a set of unrelated but widely-referenced models in different research domains under the same acronym “PAN.” These domains include turbulence modeling in computational fluid dynamics (Partially-averaged Navier–Stokes/PANS), visual attribute-informed similarity networks in computer vision, projective adversarial frameworks for medical image segmentation, statistical models for multiple imputation in multilevel data, minimal models for pan-immunity in microbial ecology, and differential privacy protocols (pan-privacy). This article provides a comprehensive review of the most prominent PAN models across fields, with theoretical and methodological detail at the level expected for readers familiar with the primary literature.

1. Partially-Averaged Navier–Stokes (PANS) Model in Turbulence Simulation

The PANS model is a variable-resolution turbulence closure approach that bridges Reynolds-Averaged Navier–Stokes (RANS) and Large Eddy Simulation (LES) by introducing tunable filter parameters $f_k$ and $f_\epsilon$ that control the fraction of resolved kinetic energy and dissipation, respectively. The underlying filtered equations are: $V_i = U_i + u_i$ with $U_i = \langle V_i \rangle$, and a sub-filter stress tensor $\tau_{ij}$.

G1-PANS (Fixed-Resolution)

Assuming constant $f_k, f_\epsilon$, the closure transports the unresolved kinetic energy $k_u$ and dissipation $\epsilon_u$: $$\frac{Dk_u}{Dt} = P_u - \epsilon_u + \nabla \cdot [(\nu + \frac{\nu_u}{\sigma_{k_u}}) \nabla k_u]$$

$$\frac{D\epsilon_u}{Dt} = C_{\epsilon1}\frac{\epsilon_u}{k_u}P_u - C^*_{\epsilon2}\frac{\epsilon_u^2}{k_u} + \nabla \cdot [(\nu + \frac{\nu_u}{\sigma_{\epsilon_u}}) \nabla \epsilon_u]$$

Transport coefficients and effective filter ratios are defined to ensure correct energy partition and grid consistency.

G2-PANS (Variable-Resolution)

When $f_k$ varies in space/time (typically to implement near-wall RANS/outer-layer LES transitions), commutation terms are mathematically derived: $P_{Tr} = \frac{k_u}{f_k} \frac{Df_k}{Dt}$

$$D_{Tr} = -\frac{k_u}{f_k} \nabla \cdot (\nu_u^* \nabla f_k) - \frac{2\nu_u^*}{f_k} \nabla k_u \cdot \nabla f_k + \frac{k_u}{f_k^2} |\nabla f_k|^2$$

These terms enter the $k_u$, $\omega_u$, and momentum equations to ensure global energy conservation and proper log-layer recovery.

Validation and Performance

On canonical separated-flow benchmarks (periodic hill, wall-mounted hump), G1- and G2-PANS reproduce mean-flow, reattachment, and Reynolds-stress statistics with errors $<5\%$ compared to LES and at 5–20x reduced computational cost. Near-wall RANS region (defined by $f_k=1$ for $y^+<y^+_{cut}$) further reduces grid demands (Razi, 2017).

2. Pairwise Attribute-informed Similarity Network (PAN) in Visual Similarity

In visual similarity and metric learning, PAN refers to the Pairwise Attribute-informed Similarity Network (Mishra et al., 2021):

Architectural Principles

• Each image is encoded via a CNN (ResNet or similar) to a feature vector $h$.
• For a pair $(x_1,x_2)$, a joint descriptor $z=|h_1-h_2|$ is computed.
• Along $M$ axes (typically semantics, e.g., color, texture), PAN predicts for each pair:
  • A similarity condition $\rho_m = \sigma(w_{1,m}^\top z + b_{1,m})$
  • A relevance weight $$\omega_m = \mathrm{softmax}(w_{2,m}^\top z + b_{2,m})$$
• The final similarity is $p(x_1, x_2) = \sum_{m=1}^M \omega_m \cdot \rho_m$.

Losses and Training

A binary cross-entropy loss is applied to the final prediction, with optional vector loss for attribute-level supervision: $$L(x_i,x_j) = \mathrm{BCE}(e_{ij}, p) + \lambda \cdot \frac1M \sum_m \mathrm{BCE}(a_{ij,m},\rho_m)$$ where $a_{ij,m}$ encodes pair-level attribute matching.

Impact

PAN achieves 4–9% improvement in compatibility prediction (Polyvore Outfits), 5% in few-shot classification (CUB), and >1% in Recall@1 for image retrieval (In-Shop Clothes) over prior metric and attribute-learning models, owing to joint modeling of attribute-matching and their per-pair relevance (Mishra et al., 2021). The model is robust with respect to batch size, backbone, and training protocol.

3. Projective Adversarial Network (PAN) for Medical Image Segmentation

In medical image segmentation, PAN describes the Projective Adversarial Network (Khosravan et al., 2019):

Core Components

• Segmentor $S$: 2D encoder–decoder CNN operating on axial slices.
• Spatial adversary ($D_s$): 2D discriminator on output slices with bottleneck attention.
• Projective adversary ($D_p$): Enforces 3D shape consistency via a projection operator:

$P((i,j),V) = 1 - \exp(-\sum_{k=1}^D V(i,j,k))$

• Attention module: Selects discriminative spatial features for $D_s$.

Losses

Combines pixel-wise BCE loss, adversarial losses on both $D_s$ (spatial) and $D_p$ (projected 3D), and generator min-max objectives.

Efficiency and Empirical Results

In pancreas segmentation (NIH TCIA), PAN achieves Dice similarity coefficient (DSC) of $85.5\%$, reducing both mean error and variability compared to state-of-the-art adversarial and recurrent CNNs. By restricting adversarial learning to 2D (with a 3D projection), PAN circumvents prohibitive memory and compute cost of full 3D GANs (Khosravan et al., 2019).

4. PAN and pan-Privacy in Differential Privacy

The term “pan-private” refers to privacy-preserving streaming algorithms that maintain differential privacy guarantees even if internal state is inspected by an adversary at any time, not just final output (Balcer et al., 2020):

Definitions

An algorithm $Q=(Q_I,Q_O)$ is $(\varepsilon,\delta)$-pan-private if, for any two adjacent streams $\vec x, \vec x'$, any prefix $t$, and any measurable set $T$,

$$\mathbb P((Q_I(\vec x_{\le t}), Q_O(Q_I(\vec x))) \in T) \le e^\varepsilon \mathbb P((Q_I(\vec x'_{\le t}), Q_O(Q_I(\vec x'))) \in T) + \delta$$

This subsumes both central DP (final output) and strong internal state privacy.

Key Results and Connections

• Distinct element counts: optimal additive error $\Theta(\sqrt{k}/\epsilon)$, tight.
• Uniformity testing (distribution property): sample complexity $\tilde O(k^{2/3})$.
• Strong reductions exist between robust shuffle privacy (distributed, adversarial tolerance) and pan-privacy (streaming/centralized), with matching lower and upper bounds for these core tasks.
• Pan-private histograms achieve $\ell_\infty$ error independent of domain size, outperforming interactive local DP (Balcer et al., 2020).

Open Questions

The alignment of lower bounds between pan-privacy and robust shuffle privacy suggests a shared set of hard tasks under adversarial or streaming access, though a general separation (beyond statistical queries over large domains) has not been established.

5. Minimal PAN Model for Pan-Immunity Maintenance by Horizontal Gene Transfer

In microbial ecology, the minimal PAN model describes the maintenance of community-wide “pan-immunity” by horizontal gene transfer (HGT) among bacteria and phages (Cui et al., 29 Feb 2024):

Model Structure

• Bacterial strains differ by subsets of defense loci; phages by matching counter-defense genes.
• Community dynamics are described by modified Lotka–Volterra equations including HGT-driven “mutation” and “injection” terms that shuffle genes among strains and phages.

Key equation for bacterial density $B_{ij}$ (carrying loci $i,j$) and matching phage $V_{ij}$: $$\dot B_{ij} = s B_{ij} - \phi \frac{B_{ij} V_{ij}}{N_B} + \frac{r_B}{2N_B} \sum_{k,\ell} B_{ik} B_{\ell j}$$

$$\dot V_{ij} = \beta \phi \frac{B_{ij} V_{ij}}{N_B} - \omega V_{ij} + \frac{r_V}{4N_B} \sum_{k,\ell} (V_{ik} B_{\ell j} + V_{\ell j} B_{ik})$$

Dynamical Regimes and Thresholds

System dynamics exhibit three regimes as HGT rate ($r$) varies:

• $r < L/N$: rapid loss of gene/genotype diversity
• $L/N < r < K/N$: persistent gene pool (pan-immunity), continuous boom–bust of genotypes
• $r > K/N$: stable coexistence of all genotypes

Critical HGT thresholds for gene and genotype persistence are derived from the effective “temperature” $\Theta$ of the population dynamics. Gene coexistence occurs for $r \gtrsim L/N_B$; genotype coexistence for $r > K/(2N_B)$.

Significance and Analogy

The model demonstrates that realistic rates of HGT, even if low, suffice to sustain the high observed diversity of defense/counter-defense genes in nature, paralleling island biogeography migration–diversity trade-offs. Even as individual strains go extinct, the distributed gene pool persists via perpetual transfer (Cui et al., 29 Feb 2024).

6. PAN for Multilevel Multiple Imputation in Statistics

The PAN model, as implemented in the R package pan, is a Bayesian data-augmentation algorithm for joint multiple imputation of missing data in multilevel/mixed-effects models (Grund et al., 2016):

Model Specification

The data model is the multivariate linear mixed-effects model: $y_{ij} = x_{ij}\beta + z_{ij}b_j + e_{ij}$ Where $b_j$ are cluster-specific random effects and $e_{ij}$ are residuals, with corresponding multivariate normal priors (or Wishart priors for covariance matrices).

Imputation Procedure

• Iterative Gibbs sampler: sample missing $y_{ij}$, then parameters $(\beta, b_j, \Sigma, \Psi)$, repeat.
• Imputed data sets are generated after burn-in/thinning.
• Analysis is conducted on each set and pooled using Rubin’s rules.

Limitations and Recommendations

• Only suitable for continuous outcomes
• The random-effects structure is identical for all imputed variables
• Convergence and diagnostics (e.g., $\hat R$, autocorrelation) are crucial due to potential slow mixing
• Auxiliary variables related to missingness are recommended to bolster the missing at random (MAR) assumption (Grund et al., 2016).

7. Summary Table: Domains and Key Aspects of PAN Models

Domain	Principal PAN Model	Core Principle / Key Mechanism
Turbulence (CFD)	Partially-Averaged Navier–Stokes	Tunable filter bridging RANS–LES–DNS
Visual Similarity	Pairwise Attribute-informed Network	Joint pairwise feature w/ relevance weights
Medical Image Segmentation	Projective Adversarial Network	2D slice segmentation + projection GAN
Statistical Imputation	MLMM Bayesian PAN (“pan” package)	DA-based joint mixed-effects MI
Microbial Ecology	Minimal PAN model (pan-immunity)	HGT-stabilized gene pool (LV+mutation)
Differential Privacy	Pan-Privacy Streaming Algorithms	State/output protection to DP levels

8. Concluding Remarks

“PAN Model” refers to several distinct advances in the scientific literature, each targeting key limitations in their respective domains—resolution control in turbulence, interpretability or attribute weighting in visual similarity, computational efficiency in segmentation, robust imputation for hierarchically structured data, ecological maintenance of distributed gene pools, and streaming privacy. While the underlying mathematical structures and objectives differ, each exploits a paradigm of partial modeling, attribute- or feature-conditional processing, or robustness under limited information exchange. These models are foundational within their areas and have set new state-of-the-art standards for accuracy, efficiency, or interpretability in benchmark tasks, with ongoing evolution in subsequent literature.