Deep Ritz revisited. - Johannes Müller; Marius Zeinhofer (CoRR, 2019)

Error Estimates for the Variational Training of Neural Networks with Boundary Penalty. - Johannes Müller; Marius Zeinhofer (CoRR, 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 Deep Ritz Method with Boundary Penalty. - Johannes Müller; Marius Zeinhofer (MSML, 2022)

Notes on Exact Boundary Values in Residual Minimisation. - Johannes Müller; Marius Zeinhofer (MSML, 2022)

The Deep Ritz Method for Parametric p-Dirichlet Problems. - Alex Kaltenbach; Marius Zeinhofer (CoRR, 2022)

An Efficient Model For Scaffold Mediated Bone Regeneration. - Patrick W. Dondl; Patrina S. P. Poh; Marius Zeinhofer (SIAM J. Appl. Math., 2022)

Achieving High Accuracy with PINNs via Energy Natural Gradient Descent. - Johannes Müller; Marius Zeinhofer (ICML, 2023)

Robin Pre-Training for the Deep Ritz Method. - Luca Courte; Marius Zeinhofer (NLDL, 2023)

Achieving High Accuracy with PINNs via Energy Natural Gradients. - Johannes Müller; Marius Zeinhofer (CoRR, 2023)

The modelling error in multi-dimensional time-dependent solute transport models. - Rami Masri; Marius Zeinhofer; Miroslav Kuchta; Marie E. Rognes (CoRR, 2023)

A Unified Framework for the Error Analysis of Physics-Informed Neural Networks. - Marius Zeinhofer; Rami Masri; Kent-André Mardal (CoRR, 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)

Position: Optimization in SciML Should Employ the Function Space Geometry. - Johannes Müller; Marius Zeinhofer (ICML, 2024)

Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks. - Felix Dangel; Johannes Müller; Marius Zeinhofer (NeurIPS, 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)

Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization. - Andrés Guzmán-Cordero; Felix Dangel; Gil Goldshlager; Marius Zeinhofer (CoRR, 2025)

Collapsing Taylor Mode Automatic Differentiation. - Felix Dangel; Tim Siebert; Marius Zeinhofer; Andrea Walther (CoRR, 2025)

Functional Neural Wavefunction Optimization. - Victor Armegioiu; Juan Carrasquilla; Siddhartha Mishra; Johannes Müller; Jannes Nys; Marius Zeinhofer; Hang Zhang (CoRR, 2025)

A priori error analysis of the proximal Galerkin method. - Brendan Keith; Rami Masri; Marius Zeinhofer (CoRR, 2025)