Cover Image

Operator learning without the adjoint

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Boullé, Nicolas, Halikias, Diana, Otto, Samuel E., Townsend, Alex
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Mathematical Guide to Operator Learning
by: Boullé, Nicolas, et al.
Published: (2023)

Transpose-free linear algebra
by: Halikias, Diana, et al.
Published: (2026)

Structure-Preserving Operator Learning
by: Bouziani, Nacime, et al.
Published: (2024)

Rational Neural Networks have Expressivity Advantages
by: Tang, Maosen, et al.
Published: (2026)

Multi-Level Monte Carlo Training of Neural Operators
by: Rowbottom, James, et al.
Published: (2025)

Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem
by: Runkel, Christina, et al.
Published: (2024)

Neural Operators with Localized Integral and Differential Kernels
by: Liu-Schiaffini, Miguel, et al.
Published: (2024)

Projection Methods for Operator Learning and Universal Approximation
by: Zappala, Emanuele
Published: (2024)

CATO: Charted Attention for Neural PDE Operators
by: Cheng, Chun-Wun, et al.
Published: (2026)

Mixture of Experts Softens the Curse of Dimensionality in Operator Learning
by: Kratsios, Anastasis, et al.
Published: (2024)

Autoregression-Free Neural Operators for Time-Dependent PDEs
by: Zhang, Jiaquan, et al.
Published: (2026)

Monte Carlo-Type Neural Operator for Differential Equations
by: Choutri, Salah Eddine, et al.
Published: (2025)

STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
by: Wang, Hong, et al.
Published: (2025)

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
by: Wang, Hong, et al.
Published: (2024)

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
by: Zhang, Ce, et al.
Published: (2023)

Mitigating spectral bias for the multiscale operator learning
by: Liu, Xinliang, et al.
Published: (2022)

Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
by: Singh, Ashutosh, et al.
Published: (2024)

Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
by: Sawhney, Medha, et al.
Published: (2025)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
by: Hou, Xianglong, et al.
Published: (2025)

PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws
by: Yang, Liu, et al.
Published: (2024)

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
by: Son, Hwijae
Published: (2025)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
by: Berner, Julius, et al.
Published: (2025)

Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study
by: Patel, Janak M., et al.
Published: (2025)

Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
by: Kapoor, Sarthak, et al.
Published: (2022)

Neural Operators as Efficient Function Interpolators
by: Niarchos, Vasilis, et al.
Published: (2026)

Universal Approximation of Nonlinear Operators and Their Derivatives
by: de Feo, Filippo
Published: (2026)

Data-Efficient Neural Operator Training via Physics-Based Active Learning
by: Polanska, Alicja, et al.
Published: (2026)

Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
by: Leng, Kuangdai, et al.
Published: (2023)

On the Convergence of Hermitian Dynamic Mode Decomposition
by: Boullé, Nicolas, et al.
Published: (2024)

A Practical Approach to Causal Inference over Time
by: Cinquini, Martina, et al.
Published: (2024)

Sparse $L^1$-Autoencoders for Scientific Data Compression
by: Chung, Matthias, et al.
Published: (2024)

Graph Neural Networks for Emulation of Finite-Element Ice Dynamics in Greenland and Antarctic Ice Sheets
by: Koo, Younghyun, et al.
Published: (2024)

Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
by: Chung, Matthias, et al.
Published: (2024)

Observation-specific explanations through scattered data approximation
by: Ghidini, Valentina, et al.
Published: (2024)

Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
by: Ke, Yekun, et al.
Published: (2024)

Low-Rank Adversarial PGD Attack
by: Savostianova, Dayana, et al.
Published: (2024)

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
by: Zhou, Hang, et al.
Published: (2024)

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
by: Li, Rui, et al.
Published: (2024)

FAST: Factorizable Attention for Speeding up Transformers
by: Gerami, Armin, et al.
Published: (2024)

AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
by: Gao, Yihang, et al.
Published: (2024)