3 citations · 3 across the 4 of their papers we have counts for
5 papers
Tensor-Train Compressed Separable PINNs: A Curvature-Aware Optimization Framework for Parametric PDEs in High Dimensions
Denis Korolev, Martin Eigel
In this work, we develop a second-order optimization framework for physics-informed neural networks (PINNs) applied to high-dimensional parametric partial differential equations (P…
Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains
Jeeeun Lee, Denis Korolev, Miro Duhovic +1
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…
Layerwise goal-oriented adaptivity for neural ODEs: an optimal control perspective
Michael Hintermüller, Michael Hinze, Denis Korolev
In this work, we propose a novel layerwise adaptive construction method for neural network architectures. Our approach is based on a goal--oriented dual-weighted residual technique…
Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures
Denis Korolev, Tim Schmidt, Dinesh K. Natarajan +4
This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challe…
A hybrid physics-informed neural network based multiscale solver as a partial differential equation constrained optimization problem
Michael Hintermüller, Denis Korolev
In this work, we study physics-informed neural networks (PINNs) constrained by partial differential equations (PDEs) and their application in approximating PDEs with two characteri…