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DOE OSTI · 3413259

WGFINNs: Weak formulation-based GENERIC formalism informed neural networks

Cheung, Siu Wun [Lawrence Livermore National Laboratory]·Park, Jun Sur·Shin, Yeonjong·Choi, Youngsoo [Lawrence Livermore National Laboratory]·Hashim, Auroni
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Cheung, Siu Wun [Lawrence Livermore National Laboratory], Park, Jun Sur, Shin, Yeonjong, Choi, Youngsoo [Lawrence Livermore National Laboratory], Hashim, Auroni. 2026-07-04. WGFINNs: Weak formulation-based GENERIC formalism informed neural networks. https://doi.org/10.1016/j.cma.2026.119213

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DOE OSTI2026

High-Order Mesh r-Adaptivity with Tangential Relaxation and Guaranteed Mesh Validity

High-order meshes are crucial for achieving optimal convergence rates in curvilinear domains, preserving symmetry, and aligning with key flow features in moving mesh simulations [1], but their quality is challenging to control. In prior work, we have developed techniques based on Target-Matrix Optimization Paradigm (TMOP) to adapt a given high-order mesh to the geometry and solution of the partial differential equation (PDE) [2, 3]. Here, we extend this framework to address two key gaps in the literature for highorder mesh 𝑟-adaptivity. First, we introduce tangential relaxation on curved surfaces using solely the discrete mesh representation, eliminating the need for access to underlying geometry (e.g., CAD model). Second, we ensure a continuously positive Jacobian determinant throughout the domain. This determinant positivity is essential for using the high-order mesh resulting from 𝑟-adaptivity with arbitrary quadrature schemes in simulations. The proposed approach is demonstrated to be robust using a variety of numerical experiments.

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