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20212026
most citedParareal with a physics-informed neural network as coarse propagator

12 citations · 29 across the 14 of their papers we have counts for

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5 papers · 1 filter

math.NA2026

Space-Time Parallel Direct Numerical Simulation of Rayleigh-Bénard Convection

Thomas Saupe, Thibaut Lunet, Mathis Bode +3

Rayleigh-Bénard convection (RBC) is a model for turbulent flow and challenging to simulate by direct numerical simulation (DNS). Adequate resolution of all relevant length scales r…

cs.LG2026

Fourier Neural Operators for Rayleigh-Bénard Convection

Chelsea Maria John, Thibaut Lunet, Sebastian Götschel +3

We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Bénard convection by predicting time increments instead of full solutions, achieving high…

math.NA2026

Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor

Vamika Rathi, Fatima Sehar, Finn Sommer +4

Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We e…

math.NA2026

Adjoint-based Perfusion Estimation from Dynamic Contrast-Enhanced Ultrasound: Advection-Diffusion and Two-Compartment Models

Sophie Externbrink, Ahmed El Kaffas, Dimitre Hristov +1

Tumor perfusion and vascular properties are important determinants of a cancer's response to therapy. In this paper, we discuss the estimation of spatially varying blood flow veloc…

stat.AP2026

Bathymetry Reconstruction by Bayesian Inference

Lars Stietz, Sebastian Götschel, Peter Schleper +1

Bathymetry reconstruction is an important problem in various fields, including oceanography and environmental monitoring. This paper presents a Bayesian inference approach to recon…