activity
20232026
most citedSeparable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning

51 citations · 61 across the 5 of their papers we have counts for

collaborators

7 papers

physics.med-ph2026

Ultra-wideband MRE of the human liver and spleen for viscoelastic model identification in hepatic inflammation

Jakob Schattenfroh, Michael Fedders, Steffen P. Häseli +10

Magnetic resonance elastography (MRE) is established for noninvasive assessment of liver fibrosis. Conventional abdominal MRE is typically limited to 40-60 Hz. Lower frequencies re…

cs.LG2025★ 1 cited

Physics-Informed Time-Integrated DeepONet: Temporal Tangent Space Operator Learning for High-Accuracy Inference

Luis Mandl, Dibyajyoti Nayak, Tim Ricken +1

Accurately modeling and inferring solutions to time-dependent partial differential equations (PDEs) over extended horizons remains a core challenge in scientific machine learning.…

physics.bio-ph2025

Insights into experimental evaluation of the non-fourier heat transfer model in biological tissues

Mohammad Azhdari, Ghader Rezazadeh, Raghav Pathak +4

A comprehensive understanding of heat transfer mechanisms in biological tissues is essential for the advancement of thermal therapeutic techniques and the development of accurate b…

math.NA2025

Least-Squares Finite Element Methods for nonlinear problems: A unified framework

Fleurianne Bertrand, Maximilian Brodbeck, Tim Ricken +1

This paper presents a unified Least-Squares framework for solving nonlinear partial differential equations by recasting the governing system as a residual minimisation problem. A L…

math.NA2024

Adaptive finite element methods based on flux and stress equilibration using FEniCSx

Maximilian Brodbeck, Fleurianne Bertrand, Tim Ricken

This contribution shows how a-posteriori error estimators based on equilibrated fluxes - H(div) functions fulfilling the underlying conservation law - can be implemented in FEniCSx…

cs.LG2024★ 51 cited

Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning

Luis Mandl, Somdatta Goswami, Lena Lambers +1

The deep operator network (DeepONet) is a popular neural operator architecture that has shown promise in solving partial differential equations (PDEs) by using deep neural networks…

cond-mat.soft2023★ 9 cited

Rheological Analysis and Evaluation of Measurement Techniques for the Curing Polymethylmethacrylate Bone Cement in Vertebroplasty

Zubin Trivedi, Jacek K. Wychowaniec, Dominic Gehweiler +6

Vertebroplasty is a minimally invasive surgical procedure used to treat vertebral fractures, which conventionally involves injecting polymethylmethacrylate (PMMA) bone cement into…