Trace Regularity PINNs: Enforcing $\mathrm{H}^{\frac{1}{2}}(\partial Ω)$ for Boundary Data

• URL: http://arxiv.org/abs/2510.16817v1
• Date: Sun, 19 Oct 2025 13:08:16 GMT
• Title: Trace Regularity PINNs: Enforcing $\mathrm{H}^{\frac{1}{2}}(\partial Ω)$ for Boundary Data
• Authors: Doyoon Kim, Junbin Song,
• Abstract summary: We propose an enhanced physics-informed neural network (PINN)<n>The Trace Regularity Physics-Informed Neural Network (TRPINN) enforces the boundary loss in the Sobolev-Slobodeckij norm $H1/2(partial Omega)$.<n>By incorporating the exact $H1/2(partial Omega)$ norm, we show that the approximation converges to the true solution in the $H1(Omega)$ sense.
• Score: 0.48342038441006796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an enhanced physics-informed neural network (PINN), the Trace Regularity Physics-Informed Neural Network (TRPINN), which enforces the boundary loss in the Sobolev-Slobodeckij norm $H^{1/2}(\partial \Omega)$, the correct trace space associated with $H^1(\Omega)$. We reduce computational cost by computing only the theoretically essential portion of the semi-norm and enhance convergence stability by avoiding denominator evaluations in the discretization. By incorporating the exact $H^{1/2}(\partial \Omega)$ norm, we show that the approximation converges to the true solution in the $H^{1}(\Omega)$ sense, and, through Neural Tangent Kernel (NTK) analysis, we demonstrate that TRPINN can converge faster than standard PINNs. Numerical experiments on the Laplace equation with highly oscillatory Dirichlet boundary conditions exhibit cases where TRPINN succeeds even when standard PINNs fail, and show performance improvements of one to three decimal digits.

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