Marius Zeinhofer

According to our database, Marius Zeinhofer authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Dynamical Measure Transport and Neural PDE Solvers for Sampling.

Jingtong Sun, Julius Berner, Lorenz Richter, Marius Zeinhofer, Johannes Müller, Kamyar Azizzadenesheli, Anima Anandkumar

CoRR, 2024

Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks.

Felix Dangel, Johannes Müller, Marius Zeinhofer

CoRR, 2024

Position: Optimization in SciML Should Employ the Function Space Geometry.

Johannes Müller, Marius Zeinhofer

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Variational Formulations of the Strong Formulation - Forward and Inverse Modeling using Isogeometric Analysis and Physics-Informed Networks.

Kent-André Mardal, Jarle Sogn, Marius Zeinhofer

CoRR, 2023

A Unified Framework for the Error Analysis of Physics-Informed Neural Networks.

Marius Zeinhofer, Rami Masri, Kent-André Mardal

CoRR, 2023

The modelling error in multi-dimensional time-dependent solute transport models.

Rami Masri, Marius Zeinhofer, Miroslav Kuchta, Marie E. Rognes

CoRR, 2023

Achieving High Accuracy with PINNs via Energy Natural Gradients.

Johannes Müller, Marius Zeinhofer

CoRR, 2023

Robin Pre-Training for the Deep Ritz Method.

Luca Courte, Marius Zeinhofer

Proceedings of the 2023 Northern Lights Deep Learning Workshop, 2023

Achieving High Accuracy with PINNs via Energy Natural Gradient Descent.

Johannes Müller, Marius Zeinhofer

Proceedings of the International Conference on Machine Learning, 2023

2022

An Efficient Model For Scaffold Mediated Bone Regeneration.

Patrick W. Dondl, Patrina S. P. Poh, Marius Zeinhofer

SIAM J. Appl. Math., 2022

The Deep Ritz Method for Parametric p-Dirichlet Problems.

Alex Kaltenbach, Marius Zeinhofer

CoRR, 2022

Notes on Exact Boundary Values in Residual Minimisation.

Johannes Müller, Marius Zeinhofer

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Error Estimates for the Deep Ritz Method with Boundary Penalty.

Johannes Müller, Marius Zeinhofer

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

Uniform Convergence Guarantees for the Deep Ritz Method for Nonlinear Problems.

Patrick W. Dondl, Johannes Müller, Marius Zeinhofer

CoRR, 2021

Error Estimates for the Variational Training of Neural Networks with Boundary Penalty.

Johannes Müller, Marius Zeinhofer

CoRR, 2021

2019

Deep Ritz revisited.

Johannes Müller, Marius Zeinhofer

CoRR, 2019