Edge Accuracy Is Not Enough: Why Dynamics-Learned Structure Fails to Transfer to Inverse Problems

Nicholas Tan Jerome; Fangnian Wang

cs.LGOct 7, 2026

Edge Accuracy Is Not Enough: Why Dynamics-Learned Structure Fails to Transfer to Inverse Problems

Authors: Nicholas Tan Jerome, Fangnian Wang

Organizations: Institute for Data Processing and Electronics (IPE) Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany · Institute for Thermal Energy Technology and Safety (ITES) Karlsruhe Institute of Technology (KIT) Karlsruhe, Germany

Abstract

A natural strategy for inverse problems with scarce labelled data is to transfer relational structure learned from abundant forward-simulation data. We show this strategy fails systematically, even when it satisfies the standard theoretical justification for why structure should help. We prove that approximate structure provides estimation-error benefits whenever the edge error satisfies $Δ< n^2 - kn$ , reducing sample complexity from $O(n^2)$ to $O(kn+Δ)$ . Structure learned via Neural Relational Inference (NRI) from dynamics prediction satisfies this condition, yet on a source-localisation task across 180 CFD-simulated hydrogen-leak scenarios and 180 acoustic scenarios, it degrades performance by 116% and 201% relative to a flexible, task-optimised attention baseline, while a physics-based prior (Green's function) degrades by only 69-72%. Four independent lines of evidence show this is not a tuning failure: NRI improves only 0.5% when given 18x more training data (versus 16.6% for the task-optimised baseline, $p<0.001$ ); performance is insensitive to the NRI edge threshold across a wide range; the dynamics-learned graph overlaps the task-optimal graph on only 6% of edges; and two further dynamics-derived structure estimators (correlation- and mutual-information-based) show no measurable benefit over a structure-free baseline, with the correlation-based estimator performing markedly worse. We formalise this gap as a statement about approximation error that the edge-accuracy condition cannot control, and we provide a lightweight transferability test (Jaccard similarity against a partially-observed target-task graph) that separates successful from failed transfer in all four domain/structure pairs we evaluate, using under an hour of computation and 15-20% of target-domain data; we present this as a heuristic calibrated on few cases, not a validated general threshold.

Figures & tables

Localiser	CFD RMSE (m) $\downarrow$	Acoustic RMSE (m) $\downarrow$
MLP (no structure)	$14.57\pm 0.24$	$4.35\pm 0.07$
Green’s Function	$8.96\pm 0.07$	$1.61\pm 0.05$
Learned Attention	$\mathbf{5.21\pm 0.22}$	$\mathbf{0.95\pm 0.04}$
NRI-Graph	$11.26\pm 0.10$	$2.86\pm 0.05$

Table 1: Structure comparison,

n=15

sensors, mean

\pm

std over 10 seeds. The ranking (Attention

>

Green’s

>

NRI

>

MLP) is preserved across two domains governed by different physics.

$m$	MLP	Green’s	Attention	NRI-Graph
10	$15.94\pm 1.28$	$8.63\pm 0.38$	$6.26\pm 0.54$	$11.32\pm 0.65$
180	$14.57\pm 0.24$	$8.96\pm 0.07$	$5.21\pm 0.22$	$11.26\pm 0.10$
Improvement	$8.6\%$	$-3.9\%$	$16.6\%$	$0.5\%^{***}$

Table 2: Data scaling: RMSE (m) as a function of training scenarios

m

(CFD domain). NRI-Graph’s improvement is an order of magnitude smaller than Attention’s despite identical data growth.

Domain	Structure	RMSE	Degradation	Est. $J$
CFD	Green’s Function	8.96 m	72%	$\approx 0.31$
CFD	NRI Learned Graph	11.26 m	116%	$\approx 0.06$
Acoustic	Green’s Function	1.61 m	69%	$\approx 0.47$
Acoustic	NRI Learned Graph	2.86 m	201%	$\approx 0.19$

Table 3: Transfer outcomes vs. estimated structural similarity to the task-optimal graph. A

J\geq 0.30

threshold separates successful from failed transfer in all four cases.

Method	RMSE (m) $\downarrow$	Sparsity	Est. $J$
MLP (no structure)	$14.57\pm 0.24$	—	0.00
NRI-Graph	$11.26\pm 0.10$	97%	$\approx 0.06$
Correlation-Graph (best $\tau$ )	$18.61\pm 0.01$	5%	$\approx 1.00$
Mutual-Information-Graph (best pct.)	$14.48\pm 0.00$	80%	$\approx 0.37$

Table 4: Two additional dynamics-learned structure baselines (CFD domain,

n=15

, mean

\pm

std over 10 seeds), neither derived from NRI. Correlation-Graph clearly underperforms the unstructured MLP; Mutual-Information-Graph is statistically indistinguishable from it. Neither provides a measurable benefit, ruling out an NRI-architecture-specific explanation for why dynamics-derived structure fails to help.

	CFD RMSE (m)		Acoustic RMSE (m)
$n$	MLP	NRI-Graph	MLP	NRI-Graph
10	14.02	12.37	3.60	3.43
20	15.17	10.53	5.19	2.41
Change	$+8\%$	$-15\%$	$+44\%$	$-30\%$

Table 5: Sensor-count scaling. The unstructured MLP degrades with more sensors in both domains; all structured methods improve, confirming that NRI-Graph’s hypothesis class is genuinely sparsity-constrained even though the constraint is misaligned with the task.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Localiser	Train	Test	Gap	Train	Test	Gap
	CFD			Acoustic
Learned Attention	5.45	5.67	$+4.1\%$	1.05	1.15	$+10.1\%$
Green’s Function	8.88	9.44	$+6.3\%$	1.62	1.61	$-0.7\%$
NRI-Graph	11.48	11.08	$-3.4\%$	3.27	3.20	$-2.0\%$
MLP (no structure)	15.39	16.92	$+10.0\%$	4.78	5.03	$+5.2\%$

Appendix

Table 6: Out-of-distribution generalisation. Green’s Function shows near-perfect acoustic robustness, consistent with its structure being derived directly from the governing physics rather than from data.

$m$	MLP	Green’s	Attention	NRI-Graph
10	$15.94\pm 1.28$	$8.63\pm 0.38$	$6.26\pm 0.54$	$11.32\pm 0.65$
25	$15.54\pm 0.46$	$8.02\pm 0.28$	$5.26\pm 0.39$	$11.32\pm 0.29$
50	$17.84\pm 0.47$	$9.61\pm 0.21$	$5.76\pm 0.21$	$12.10\pm 0.27$
100	$16.68\pm 0.38$	$9.35\pm 0.17$	$5.75\pm 0.16$	$11.58\pm 0.19$
150	$15.08\pm 0.20$	$9.25\pm 0.15$	$5.45\pm 0.17$	$11.61\pm 0.15$
180	$14.57\pm 0.24$	$8.96\pm 0.07$	$5.21\pm 0.22$	$11.26\pm 0.10$

Appendix

Table 7: Full data-scaling curve (CFD domain), RMSE (m) as a function of training scenarios

m

, mean

\pm

std over 10 seeds.

Configuration	Avg. degree	Sparsity	RMSE (m)
$\tau=0.1$	1.1	97%	$11.50\pm 0.07$
$\tau\in[0.2,0.5]$	1.0	97%	$11.49\pm 0.11$
$\tau=0.7$ (disconnected)	0.0	100%	$17.42\pm 0.13$
Top-2 connectivity	2.0	94%	$14.42\pm 0.20$
Top-4 connectivity	4.0	88%	$13.79\pm 0.16$

Appendix

Table 8: NRI-Graph ablation over edge threshold

\tau

and top-

k

connectivity (CFD domain). Performance is flat across

\tau\in[0.2,0.5]

and degrades under denser top-

k

connectivity, ruling out a simple sparsity-tuning explanation.

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Center for Quantum Physics and Intelligent Sciences, Department of Physics, Capital Normal University, Beijing 10048, China

Spatial Photonic Ising Machines Out-Of-Distribution Neural Reconstruction Magnetic Tunnel Junction

Open paper AskPDF arXiv

Neural networks are increasingly used to infer hidden physical structure from dynamical observations, yet it remains unclear whether their out-of-distribution performance reflects transferable physical rule learning. We address this question in a controlled inverse problem: reconstructing interaction graphs of a kinetic Ising model from Glauber magnetization trajectories. Across convolutional, graph, Transformer, and hybrid architectures, we find that data-driven training produces distinct and reproducible statistical strategies under topology and temperature shifts. Edge-population diagnostics reveal that Transformer-based models tend to preserve the link density of the training ensemble, whereas convolutional models can collapse toward sparse- or no-link predictions that appear out-of-distribution stable by exploiting the majority no-link class. Thus, high in-distribution accuracy and apparent out-of-distribution robustness do not necessarily imply a learned dynamics-to-structure rule. Instead, neural reconstruction can be governed by architecture-dependent statistical priors. Our results identify a concrete failure mode of standard data-driven learning in physical inverse problems and motivate rule-guided principles for machine-learning-assisted scientific discovery.

Abstract

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