A variational approach for the correction of field-inhomogeneities in EPI sequences. - Janine Olesch; Lars Ruthotto; Harald Kugel; Stefan Skare; Bernd Fischer 001; Carsten H. Wolters (Image Processing, 2010)

A Simplified Pipeline for Motion Correction in Dual Gated Cardiac PET. - Lars Ruthotto; Fabian Gigengack; Martin Burger 001; Carsten H. Wolters; Xiaoyi Jiang 001; Klaus P. Schäfers; Jan Modersitzki (Bildverarbeitung für die Medizin, 2012)

Atlas-Based Whole-Body PET-CT Segmentation Using a Passive Contour Distance. - Fabian Gigengack; Lars Ruthotto; Xiaoyi Jiang 001; Jan Modersitzki; Martin Burger 001; Sven Hermann; Klaus P. Schäfers (MCV, 2012)

Registration of Dynamic Contrast Enhanced MRI with Local Rigidity Constraint. - Lars Ruthotto; Erlend Hodneland; Jan Modersitzki (WBIR, 2012)

Motion Correction in Dual Gated Cardiac PET Using Mass-Preserving Image Registration. - Fabian Gigengack; Lars Ruthotto; Martin Burger 001; Carsten H. Wolters; Xiaoyi Jiang 001; Klaus P. Schäfers (IEEE Trans. Medical Imaging, 2012)

Hyperelastic image registration: theory, numerical methods, and applications. - Lars Ruthotto (2012)

Hyperelastic Susceptibility Artifact Correction of DTI in SPM. - Lars Ruthotto; Siawoosh Mohammadi; Constantin Heck; Jan Modersitzki; Nikolaus Weiskopf (Bildverarbeitung für die Medizin, 2013)

A Hyperelastic Regularization Energy for Image Registration. - Martin Burger 001; Jan Modersitzki; Lars Ruthotto (SIAM J. Sci. Comput., 2013)

Model-Based Parameterestimation in DCE-MRI Without an Arterial Input Function. - Constantin Heck; Lars Ruthotto; Jan Modersitzki; Benjamin Berkels (Bildverarbeitung für die Medizin, 2014)

A new method for joint susceptibility artefact correction and super-resolution for dMRI. - Lars Ruthotto; Siawoosh Mohammadi; Nikolaus Weiskopf (Image Processing, 2014)

Geophysical Imaging of Fluid Flow in Porous Media. - Jennifer Fohring; Eldad Haber; Lars Ruthotto (SIAM J. Sci. Comput., 2014)

Non-linear Image Registration. - Lars Ruthotto; Jan Modersitzki (Handbook of Mathematical Methods in Imaging, 2015)

Combined Background Field Removal and Reconstruction for Quantitative Susceptibility Mapping. - Maximilian März; Lars Ruthotto (Bildverarbeitung für die Medizin, 2016)

Stable Architectures for Deep Neural Networks. - Eldad Haber; Lars Ruthotto (CoRR, 2017)

Learning across scales - A multiscale method for Convolution Neural Networks. - Eldad Haber; Lars Ruthotto; Elliot Holtham (CoRR, 2017)

LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables. - James L. Herring; James G. Nagy; Lars Ruthotto (CoRR, 2017)

A stabilized multigrid solver for hyperelastic image registration. - Lars Ruthotto; Chen Greif; Jan Modersitzki (Numer. Linear Algebra Appl., 2017)

A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration. - Andreas Mang; Lars Ruthotto (SIAM J. Sci. Comput., 2017)

jInv-a Flexible Julia Package for PDE Parameter Estimation. - Lars Ruthotto; Eran Treister; Eldad Haber (SIAM J. Sci. Comput., 2017)

Reversible Architectures for Arbitrarily Deep Residual Neural Networks. - Bo Chang 002; Lili Meng; Eldad Haber; Lars Ruthotto; David Begert; Elliot Holtham (AAAI, 2018)

Learning Across Scales - Multiscale Methods for Convolution Neural Networks. - Eldad Haber; Lars Ruthotto; Elliot Holtham; Seong-Hwan Jun (AAAI, 2018)

Low-Cost Parameterizations of Deep Convolution Neural Networks. - Eran Treister; Lars Ruthotto; Michal Sharoni; Sapir Zafrani; Eldad Haber (CoRR, 2018)

Never look back - A modified EnKF method and its application to the training of neural networks without back propagation. - Eldad Haber; Felix Lucka; Lars Ruthotto (CoRR, 2018)

Improved Susceptibility Artifact Correction of Echo-Planar MRI using the Alternating Direction Method of Multipliers. - Jan MacDonald; Lars Ruthotto (J. Math. Imaging Vis., 2018)

Optimal Experimental Design for Inverse Problems with State Constraints. - Lars Ruthotto; Julianne Chung; Matthias Chung (SIAM J. Sci. Comput., 2018)

IMEXnet A Forward Stable Deep Neural Network. - Eldad Haber; Keegan Lensink; Eran Treister; Lars Ruthotto (ICML, 2019)

Large-Scale Classification using Multinomial Regression and ADMM. - Samy Wu Fung; Sanna Tyrväinen; Lars Ruthotto; Eldad Haber (CoRR, 2019)

LeanResNet: A Low-cost yet Effective Convolutional Residual Networks. - Jonathan Ephrath; Lars Ruthotto; Eldad Haber; Eran Treister (CoRR, 2019)

A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems. - Lars Ruthotto; Stanley J. Osher; Wuchen Li; Levon Nurbekyan; Samy Wu Fung (CoRR, 2019)

A multiscale method for model order reduction in PDE parameter estimation. - Samy Wu Fung; Lars Ruthotto (J. Comput. Appl. Math., 2019)

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research. - Karsten Tabelow; Evelyne Balteau; John Ashburner; Martina F. Callaghan; Bogdan Draganski; Gunther Helms; Ferath Kherif; Tobias Leutritz; Antoine Lutti; Christophe Phillips; Enrico Reimer; Lars Ruthotto; et al. (NeuroImage, 2019)

An Uncertainty-Weighted Asynchronous ADMM Method for Parallel PDE Parameter Estimation. - Samy Wu Fung; Lars Ruthotto (SIAM J. Sci. Comput., 2019)

Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows. - Derek Onken; Lars Ruthotto (CoRR, 2020)

Multigrid-in-Channels Neural Network Architectures. - Moshe Eliasof; Jonathan Ephrath; Lars Ruthotto; Eran Treister (CoRR, 2020)

Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization. - Kelvin K. Kan; James G. Nagy; Lars Ruthotto (CoRR, 2020)

Deep Neural Networks Motivated by Partial Differential Equations. - Lars Ruthotto; Eldad Haber (J. Math. Imaging Vis., 2020)

LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks. - Jonathan Ephrath; Moshe Eliasof; Lars Ruthotto; Eldad Haber; Eran Treister (IEEE J. Sel. Top. Signal Process., 2020)

Layer-Parallel Training of Deep Residual Neural Networks. - Stefanie Günther; Lars Ruthotto; Jacob B. Schroder; Eric C. Cyr; Nicolas R. Gauger (SIAM J. Math. Data Sci., 2020)

Gauss-Newton Optimization for Phase Recovery From the Bispectrum. - James L. Herring; James G. Nagy; Lars Ruthotto (IEEE Trans. Computational Imaging, 2020)

OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport. - Derek Onken; Samy Wu Fung; Xingjian Li 005; Lars Ruthotto (AAAI, 2021)

A Neural Network Approach Applied to Multi-Agent Optimal Control. - Derek Onken; Levon Nurbekyan; Xingjian Li 005; Samy Wu Fung; Stanley J. Osher; Lars Ruthotto (ECC, 2021)

An Introduction to Deep Generative Modeling. - Lars Ruthotto; Eldad Haber (CoRR, 2021)

PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization. - Kelvin K. Kan; Samy Wu Fung; Lars Ruthotto (SIAM J. Sci. Comput., 2021)

Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection. - Elizabeth Newman; Lars Ruthotto; Joseph L. Hart; Bart G. van Bloemen Waanders (SIAM J. Math. Data Sci., 2021)

Multivariate Quantile Function Forecaster. - Kelvin Kan; François-Xavier Aubet; Tim Januschowski; Youngsuk Park; Konstantinos Benidis; Lars Ruthotto; Jan Gasthaus (AISTATS, 2022)

ωGNNs: Deep Graph Neural Networks Enhanced by Multiple Propagation Operators. - Moshe Eliasof; Lars Ruthotto; Eran Treister (CoRR, 2022)

'hessQuik': Fast Hessian computation of composite functions. - Elizabeth Newman; Lars Ruthotto (J. Open Source Softw., 2022)

slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks. - Elizabeth Newman; Julianne Chung; Matthias Chung; Lars Ruthotto (SIAM J. Sci. Comput., 2022)

Improving Graph Neural Networks with Learnable Propagation Operators. - Moshe Eliasof; Lars Ruthotto; Eran Treister (ICML, 2023)

Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation. - Paul Hagemann; Lars Ruthotto; Gabriele Steidl; Nicole Tianjiao Yang (CoRR, 2023)

Alternating Minimization for Regression with Tropical Rational Functions. - Alex Dunbar; Lars Ruthotto (CoRR, 2023)

Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics. - Malvern Madondo; Deepanshu Verma; Lars Ruthotto; Nicholas Au Yong (CoRR, 2023)

A Neural Network Approach for High-Dimensional Optimal Control Applied to Multiagent Path Finding. - Derek Onken; Levon Nurbekyan; Xingjian Li 005; Samy Wu Fung; Stanley J. Osher; Lars Ruthotto (IEEE Trans. Control. Syst. Technol., 2023)

MGIC: Multigrid-in-Channels Neural Network Architectures. - Moshe Eliasof; Jonathan Ephrath; Lars Ruthotto; Eran Treister (SIAM J. Sci. Comput., 2023)

Differential Equations for Continuous-Time Deep Learning. - Lars Ruthotto (CoRR, 2024)

PyHySCO: GPU-Enabled Susceptibility Artifact Distortion Correction in Seconds. - Abigail Julian; Lars Ruthotto (CoRR, 2024)

A Neural Network Approach for Stochastic Optimal Control. - Xingjian Li 005; Deepanshu Verma; Lars Ruthotto (SIAM J. Sci. Comput., 2024)

Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference. - Zheyu Oliver Wang; Ricardo Baptista; Youssef Marzouk 001; Lars Ruthotto; Deepanshu Verma (SIAM J. Sci. Comput., 2025)

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling. - Haley Rosso; Lars Ruthotto; Khachik Sargsyan (CoRR, 2025)