Author

Denis Korolev

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#machine learning Preprint Sep 2026

Tensor-Train Compressed Separable PINNs: A Curvature-Aware Optimization Framework for Parametric PDEs in High Dimensions

In this work, we develop a second-order optimization framework for physics-informed neural networks (PINNs) applied to high-dimensional parametric partial differential equations (PDEs). The framework is built on the Gauss--Newton pullback metric, which provides an operator-informed notion of curvature in parameter spac...

Denis Korolev, Martin Eigel · 0 citations

Preprint Aug 2026

Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains

In this work, viscous fluid flow governed by the Stokes equations in highly perforated domains is studied using physics-informed neural networks (PINNs). Perforated microstructures induce complex boundary conditions and fine-scale flow features that are difficult for standard neural networks to resolve. Conventional PI...

Jeeeun Lee, Denis Korolev, M. Duhovic et al. · 0 citations