Hayden Schaeffer

University of California, Los Angeles — United States

Physics and Astronomy · Physical Sciences

15h-index1.7kcitations86works2.42yr avg
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Research Topics

Model Reduction and Neural Networks(29), Sparse and Compressive Sensing Techniques(20), Neural Networks and Applications(14), Image and Signal Denoising Methods(8), Medical Image Segmentation Techniques(7)

Publications86 total

PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations
arXiv (Cornell University)·2025Open Access
PI-MFM: Physics-informed multimodal foundation model for solving partial differential equations
ArXiv.org·2025Open Access
Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
arXiv (Cornell University)·2025Open Access
Regularized Random Fourier Features and Finite Element Reconstruction for Operator Learning in Sobolev Space
ArXiv.org·2025Open Access
DeepONet as a multi-Operator extrapolation model: Distributed pretraining with physics-Informed fine-Tuning
Journal of Computational Physics·2025
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions
Journal of Machine Learning·2025· 1 citedOpen Access
Finite Element Representation Network (FERN) for Operator Learning with a Localized Trainable Basis
arXiv (Cornell University)·2025Open Access
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
arXiv (Cornell University)·2025Open Access
Towards a foundation model for partial differential equations: Multioperator learning and extrapolation
Physical review. E·2025· 12 cited
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions
arXiv (Cornell University)·2025· 1 citedOpen Access
BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics
arXiv (Cornell University)·2025Open Access
TIME-SERIES FORECASTING AND REFINEMENT WITHIN A MULTIMODAL PDE FOUNDATION MODEL
Journal of Machine Learning for Modeling and Computing·2025· 1 cited
Deeponet as a Multi-Operator Extrapolation Model Distributed Pretraining with Physics-Informed Fine-Tuning
SSRN Electronic Journal·2025Open Access
Inexact Proximal Point Algorithms for Zeroth-Order Global Optimization
arXiv (Cornell University)·2024Open Access
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
arXiv (Cornell University)·2024Open Access
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
arXiv (Cornell University)·2024Open Access
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
arXiv (Cornell University)·2024· 1 citedOpen Access
PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers
Neural Networks·2024· 9 cited
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model
arXiv (Cornell University)·2024Open Access
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
arXiv (Cornell University)·2024Open Access
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Frequent Co-authors

Giang Tran(12), Stanley Osher(9), Linan Zhang(8), Rachel Ward(8), Zecheng Zhang(6), Luminita A. Vese(5), Bobby Shi(3), Zecheng Zhang(3), Scott G. McCalla(2), Russel E. Caflisch(2), Abolfazl Hashemi(2), Ufuk Topcu(2), William M. Feldman(2), Christian Moya(2), Guang Lin(2), Thomas Y. Hou(2), Jingmin Sun(2), Yi Yang(2), Nóirín Duggan(2), Carole Le Guyader(2)