Cova-PINN is a multi-domain physics-informed neural network designed to model fluid–solid conjugate heat transfer in complex geometries. It aligns conservation support with thermal interaction paths, jointly optimizing cross-domain composite control‑volume balances at the local scale and paired‑wall closure at the global exchanger scale. In tests on four triply periodic minimal surface heat exchangers and a DualMS design, Cova‑PINN reduces average outlet‑temperature and device‑level closure errors by 37.7 % and 60.2 % compared to the closest baseline, while improving full‑field and heat‑duty accuracy.
arXiv:2603. 08465v3 Announce Type: replace Abstract: While Physics-Informed Neural Networks (PINNs) offer a mesh-free approach to solving fluid-flow PDEs, standard point-wise residual minimization suffers from convergence pathologies in topologically complex domains like Triply Periodic Minimal Surfaces (TPMS).
arXiv:2606. 07982v1 Announce Type: new Abstract: High-dimensional transient heat diffusion under noisy boundary conditions exposes a fundamental limitation of classical numerical methods: accuracy degrades catastrophically where physical noise is unavoidable.
arXiv:2609.14841v1 Announce Type: new Abstract: Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability w...
The paper introduces a hybrid modeling framework that couples numerics-informed neural networks (NINNs) with classical full order models (FOMs) via the overlapping Schwarz alternating method. It demonstrates that a monolithic NINN can be accurately trained on a two-dimensional advection-diffusion problem in a high Peclet-number regime without domain decomposition, and then uses overlapping multiplicative Schwarz to couple a pre-trained, subdomain-local NINN with a neighboring FOM while keeping the NINN weights fixed during iteration. Two training strategies—top-down, using full-domain FOM-FOM Schwarz data, and bottom-up, generating synthetic boundary traces—both yield hybrid NINN-FOM solutions that closely match the corresponding FOM-FOM Schwarz results.
A new AI-accelerated iterative solver, DAIST, is introduced for 3D-IC thermal modeling. It decomposes a full-package FEM simulation into block-level subdomains, replaces each subdomain solver with a neural operator, and couples them through iterative exchanges of interfacial temperature and heat flux. This approach eliminates the need to retrain models when die count or topology changes and offers a controllable accuracy‑runtime tradeoff, achieving up to 178× speedup with minimal temperature error on tested systems.
The paper presents cross‑domain pretraining for neural surrogates in steady‑state computational fluid dynamics (CFD). By training models across varied geometries, boundary conditions, and fidelities, the authors show that such pretraining improves zero‑ and few‑shot performance compared to training from scratch or transferring from domain‑specific experts. Finetuning a pretrained cross‑domain model can reduce errors by 2–3× at the same sample size and require eight times fewer samples to reach the same error, with benefits that grow with model size and dataset diversity.
arXiv:2601.12971v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) can be limited by coordinate representations and conflicting gradients from heterogeneous physical constra...
arXiv:2606. 18032v1 Announce Type: cross Abstract: We propose a new weak-form Physics-Informed Neural Network approach (named INI-VPINN).
arXiv:2608. 08322v1 Announce Type: cross Abstract: Physics-informed neural networks applied to the level-set formulation of interface advection commonly augment the residual and initial-condition losses with an eikonal regulariser, penalising the deviation of $\|\nabla\phi\|$ from unity.
arXiv:2607. 14233v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE).
This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and scalar transport equations. Such systems, found in applications like turbidity currents and thermal convection, feature strong nonlinear coupling and multiscale behavior that make high-fidelity simulations computationally expensive.
arXiv:2607. 03682v1 Announce Type: cross Abstract: Convection-dominated convection-diffusion problems often develop thin layers, where the solution has sharp transition profiles and its derivatives are highly localized.