Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
- URL: http://arxiv.org/abs/2605.23391v1
- Date: Fri, 22 May 2026 09:01:31 GMT
- Title: Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
- Abstract summary: Physics-informed neural networks (PINNs) for coupled multiphysics systems suffer systematic accuracy degradation as inter-equation coupling strengthens.<n>We provide a theoretical explanation for this phenomenon through neural tangent kernel (NTK) analysis.
- Score: 12.079987397544953
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Physics-informed neural networks (PINNs) for coupled multiphysics systems suffer systematic accuracy degradation as inter-equation coupling strengthens. We provide a theoretical explanation for this phenomenon through neural tangent kernel (NTK) analysis: for linearly coupled systems, we prove that the standard NTK's spectral radius grows as $Ω(γ^2)$ with coupling strength $γ$, shrinking the stable learning rate, while block-diagonal Gauss--Newton (GN) preconditioning yields a preconditioned NTK $K_P = J H^{+} J^\top$ (where $H$ is the block-diagonal GN Hessian) whose spectral radius is bounded by $S$ ($S$ = number of networks), independent of $γ$. We verify the $Ω(γ^2)$ growth numerically across symmetric, asymmetric, and nonlinear coupled PDE systems, and confirm $λ_{\max}(K_P) = S$ with equality in all cases. Combining the Kronecker-preconditioned optimizer SOAP with inverse-gradient-norm loss balancing (SOAP+GN) yields coupling-robust accuracy: across 234 experiments spanning three 1D systems of increasing nonlinearity and a 2D electroosmotic flow benchmark, SOAP+GN maintains final-epoch $L_2$ degradation $\leq 1.1\times$ (ratio of strong- to weak-coupling error) even as coupling parameters vary over one to two orders of magnitude, compared with $> 10^2\times$ for Adam+GN. SOAP+GN further scales to a 2D, 6-PDE electroosmotic flow system at EDL-resolved conditions -- a regime that all prior PINN electrokinetics studies have avoided through simplified physics -- where Adam+GN fails entirely ($L_2 > 0.9$).
Related papers
- A variational physics-informed graph neural network for heterogeneous solid mechanics [0.0]
This work presents a variational, label-free physics-informed graph neural network (PI-GNN) in which the conforming is carried by the discretization rather than by the trial field.
arXiv Detail & Related papers (2026-09-10T02:02:32Z) - Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise [49.730496294398726]
We develop a novel quantum mean estimator for heavy-tailed random variables.<n>We derive stronger lower bounds for tail index>4/3$, showing that a nontrivial dependence on a dimension is unavoidable.<n>For a convex objective function, we propose a quantum projected gradient descent method.
arXiv Detail & Related papers (2026-07-28T09:29:37Z) - Unsupervised Discovery of Intermediate Phase Order in the Frustrated $J_1$-$J_2$ Heisenberg Model via Prometheus Framework [0.0]
We apply the Prometheus variational autoencoder framework to explore the $J_1$-$J$ phase diagram.<n>We identify the structure factor $S(,)$ and $S(,)$ as the dominant order parameters.<n>This work establishes a scalable pathway for applying machine learning to frustrated quantum systems.
arXiv Detail & Related papers (2026-02-25T00:44:51Z) - Entanglement-Dependent Error Bounds for Hamiltonian Simulation [0.0]
We prove that the first-order Trotter error scales as $mathcalO(t2 S_textmax operatornamepolylog(n)/r)$ rather than the worst-case $mathcalO(t2 n/r)$.<n>These results have immediate applications to quantum chemistry, condensed matter simulation, and resource estimation for fault-tolerant quantum computing.
arXiv Detail & Related papers (2026-01-31T06:34:37Z) - Emergence and scaling laws in SGD learning of shallow neural networks [64.48316762675141]
We study the complexity of online gradient descent (SGD) for learning a two-layer neural network with $P$ neurons on isotropic Gaussian data.<n>We provide a precise analysis of SGD dynamics for the training of a student two-layer network to minimize the mean squared error (MSE) objective.
arXiv Detail & Related papers (2025-04-28T16:58:55Z) - Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Ancilla quantum measurements on interacting chains: Sensitivity of entanglement dynamics to the type and concentration of detectors [46.76612530830571]
We consider a quantum many-body lattice system that is coupled to ancillary degrees of freedom (detectors'')
We explore the dynamics of density and of entanglement entropy in the chain, for various values of $rho_a$ and $M$.
arXiv Detail & Related papers (2023-11-21T21:41:11Z) - Bounding the Width of Neural Networks via Coupled Initialization -- A
Worst Case Analysis [121.9821494461427]
We show how to significantly reduce the number of neurons required for two-layer ReLU networks.
We also prove new lower bounds that improve upon prior work, and that under certain assumptions, are best possible.
arXiv Detail & Related papers (2022-06-26T06:51:31Z) - Optimal Robust Linear Regression in Nearly Linear Time [97.11565882347772]
We study the problem of high-dimensional robust linear regression where a learner is given access to $n$ samples from the generative model $Y = langle X,w* rangle + epsilon$
We propose estimators for this problem under two settings: (i) $X$ is L4-L2 hypercontractive, $mathbbE [XXtop]$ has bounded condition number and $epsilon$ has bounded variance and (ii) $X$ is sub-Gaussian with identity second moment and $epsilon$ is
arXiv Detail & Related papers (2020-07-16T06:44:44Z) - Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings.
In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$.
We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
arXiv Detail & Related papers (2020-02-25T19:18:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.