Hayden Schaeffer

According to our database, Hayden Schaeffer authored at least 42 papers between 2013 and 2025.

Collaborative distances:

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

2025

A discretization-invariant extension and analysis of some deep operator networks.

Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer

J. Comput. Appl. Math., 2025

2024

PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers.

Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

Neural Networks, 2024

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE.

Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer

Multiscale Model. Simul., 2024

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction.

Yadi Cao, Yuxuan Liu, Liu Yang, Rose Yu, Hayden Schaeffer, Stanley J. Osher

CoRR, 2024

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning.

Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer

CoRR, 2024

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study.

Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer

CoRR, 2024

Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model.

Derek Jollie, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics.

Yuxuan Liu, Jingmin Sun, Xinjie He, Griffin Pinney, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

LeMON: Learning to Learn Multi-Operator Networks.

Jingmin Sun, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation.

Jingmin Sun, Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

2023

D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators.

Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer

CoRR, 2023

PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers.

Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

CoRR, 2023

2022

BelNet: Basis enhanced learning, a mesh-free neural operator.

Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer

CoRR, 2022

Random Feature Models for Learning Interacting Dynamical Systems.

Yuxuan Liu, Scott G. McCalla, Hayden Schaeffer

CoRR, 2022

SRMD: Sparse Random Mode Decomposition.

Nicholas Richardson, Hayden Schaeffer, Giang Tran

CoRR, 2022

HARFE: Hard-Ridge Random Feature Expansion.

Esha Saha, Hayden Schaeffer, Giang Tran

CoRR, 2022

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning.

Yuege Xie, Robert Shi, Hayden Schaeffer, Rachel A. Ward

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Concentration of Random Feature Matrices in High-Dimensions.

Zhijun Chen, Hayden Schaeffer, Rachel A. Ward

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning.

Yuege Xie, Bobby Shi, Hayden Schaeffer, Rachel A. Ward

CoRR, 2021

Conditioning of Random Feature Matrices: Double Descent and Generalization Error.

Zhijun Chen, Hayden Schaeffer

CoRR, 2021

Function Approximation via Sparse Random Features.

Abolfazl Hashemi, Hayden Schaeffer, Robert Shi, Ufuk Topcu, Giang Tran, Rachel A. Ward

CoRR, 2021

Reduced Order Modeling using Shallow ReLU Networks with Grassmann Layers.

Kayla Bollinger, Hayden Schaeffer

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

Extending the Step-Size Restriction for Gradient Descent to Avoid Strict Saddle Points.

Hayden Schaeffer, Scott G. McCalla

SIAM J. Math. Data Sci., 2020

Extracting Structured Dynamical Systems Using Sparse Optimization With Very Few Samples.

Hayden Schaeffer, Giang Tran, Rachel A. Ward, Linan Zhang

Multiscale Model. Simul., 2020

Forward Stability of ResNet and Its Variants.

Linan Zhang, Hayden Schaeffer

J. Math. Imaging Vis., 2020

Recovery guarantees for polynomial coefficients from weakly dependent data with outliers.

Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel A. Ward

J. Approx. Theory, 2020

NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data.

Yifan Sun, Linan Zhang, Hayden Schaeffer

Proceedings of Mathematical and Scientific Machine Learning, 2020

2019

On the Convergence of the SINDy Algorithm.

Linan Zhang, Hayden Schaeffer

Multiscale Model. Simul., 2019

2018

Extracting Sparse High-Dimensional Dynamics from Limited Data.

Hayden Schaeffer, Giang Tran, Rachel A. Ward

SIAM J. Appl. Math., 2018

Recovery guarantees for polynomial approximation from dependent data with outliers.

Lam Si Tung Ho, Hayden Schaeffer, Giang Tran, Rachel A. Ward

CoRR, 2018

2016

An Accelerated Method for Nonlinear Elliptic PDE.

Hayden Schaeffer, Thomas Y. Hou

J. Sci. Comput., 2016

2015

Real-Time Adaptive Video Compression.

Hayden Schaeffer, Yi Yang, Hongkai Zhao, Stanley J. Osher

SIAM J. Sci. Comput., 2015

Space-Time Regularization for Video Decompression.

Hayden Schaeffer, Yi Yang, Stanley J. Osher

SIAM J. Imaging Sci., 2015

An L¹ Penalty Method for General Obstacle Problems.

Giang Tran, Hayden Schaeffer, William M. Feldman, Stanley J. Osher

SIAM J. Appl. Math., 2015

Sparse + low-energy decomposition for viscous conservation laws.

Thomas Y. Hou, Qin Li, Hayden Schaeffer

J. Comput. Phys., 2015

2014

On the Compressive Spectral Method.

Alan Mackey, Hayden Schaeffer, Stanley J. Osher

Multiscale Model. Simul., 2014

Variational Dynamics of Free Triple Junctions.

Hayden Schaeffer, Luminita A. Vese

J. Sci. Comput., 2014

Active Contours with Free Endpoints.

Hayden Schaeffer, Luminita A. Vese

J. Math. Imaging Vis., 2014

2013

Variational Models for Fine Structures.

Hayden Schaeffer

PhD thesis, 2013

A Low Patch-Rank Interpretation of Texture.

Hayden Schaeffer, Stanley J. Osher

SIAM J. Imaging Sci., 2013

Boundary detection in echocardiography using a Split Bregman edge detector and a topology preserving level set approach.

Nóirín Duggan, Hayden Schaeffer, Carole Le Guyader, Edward Jones, Martin Glavin, Luminita A. Vese

Proceedings of the 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013

Mixing space-time derivatives for video compressive sensing.

Yi Yang, Hayden Schaeffer, Wotao Yin, Stanley J. Osher

Proceedings of the 2013 Asilomar Conference on Signals, 2013