5 papers
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.…
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…
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…
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…
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…