Latest Neural Operators (FNO, DeepONet) Research Papers
The newest Neural Operators (FNO, DeepONet) papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Neural Operators (FNO, DeepONet) so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
Get the latest Neural Operators (FNO, DeepONet) papers in your inbox — free →Recent papers
- LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style UpdatesDmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…
- Learning Nonlinear Responses in PET Bottle Buckling With a Hybrid DeepONet–Transolver FrameworkVarun Kumar, Jing Bi, Cyril Ngo Ngoc, Victor Oancea et al. · International Journal for N... · Sep 2, 2026
ABSTRACT Neural surrogates and operator networks for solving partial differential equation (PDE) problems have attracted significant research interest in recent years. However, most existing approaches are limited in their ability to genera…
- DVL-DeepONet: A physics-guided operator learning for resilient underwater navigationArup Kumar Sahoo, Itzik Klein · Ocean Engineering · Aug 31, 2026
- Euclidean Fourier Neural OperatorsNathanael Bosch, Niklas Frederik Schmitz, Michael F. Herbst · arXiv · Aug 28, 2026
Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent of the grid resolution at which they are trained and evaluated. However, FNOs are not ind…
- Enforcing Dirichlet Boundary Conditions in Operator LearningAndrew M. Stuart, Margaret Trautner · arXiv · Aug 27, 2026
Operator learning in scientific machine learning is concerned with approximation of maps between infinite-dimensional function spaces; such maps frequently arise as the solution operators of partial differential equations (PDEs). Neural ope…
- Inertial Manifold Neural Operator for Dissipative Time-Dependent Partial Differential EquationsXiaoyang Xie, Clarence W. Rowley · arXiv · Aug 24, 2026
In this paper, we introduce the Inertial Manifold Neural Operator (IMNO) for solving dissipative time-dependent partial differential equations (PDEs). The long-time dynamics of such systems often exhibit an effective low-dimensional structu…
- On the Fragility of Self-Improving Agents: Variance, Task Order, and UnderspecificationQinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang et al. · arXiv · Aug 18, 2026
Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods…
- Reproducibility package for CartoARG:Cartographic Attribution and Reasoning with Grounded Evidence for Auditable Cross-Scale Road MatchingWenyue Guo · Figshare · Aug 15, 2026
This item is the frozen v1.3 reproducibility package accompanying the manuscript “CartoARG: Cartographic Attribution and Reasoning with Grounded Evidence for Auditable Cross-Scale Road Matching”.CartoARG treats cross-scale road matching as …
- Morphology-, noise-, and resolution-robust ultrasound elasticity imaging with Fourier neural operator.Heekyu Kim, H Lee, Minwoo Park, Seunghwa Ryu · PubMed · Aug 15, 2026
Quasi-static strain elastography is a non-invasive technique for estimating tissue stiffness fields from displacement fields obtained by comparing ultrasound signals before and after compression. While recent deep learning approaches have e…
- Reproducibility package for CartoARG:Cartographic Attribution and Reasoning with Grounded Evidence for Auditable Cross-Scale Road MatchingWenyue Guo · Figshare · Aug 15, 2026
This item is the frozen v1.3 reproducibility package accompanying the manuscript “CartoARG: Cartographic Attribution and Reasoning with Grounded Evidence for Auditable Cross-Scale Road Matching”.CartoARG treats cross-scale road matching as …
- Fourier neural operator-based asphalt concrete pavement response modelingZezhen Dong, Tianqing Hei, Tianxiang Bu, Siqi Huang et al. · International Journal of Pa... · Aug 13, 2026
Mechanical response modeling of asphalt pavements is essential for both pavement design and maintenance. However, neural network-based approaches face challenges in acquiring high-fidelity data and ensuring model generalization. To address …
- Surrogate modeling of drift-reduced Braginskii turbulence with resistivity-conditioned Koopman neural operatorsA. Shaa, Kyungtak Lim, Long Shan Chan, Claude Guet · Plasma Science and Technology · Aug 7, 2026
Abstract Machine-learning-driven surrogate operators are developed for three-dimensional, nonlinear, flux-driven simulations of boundary plasma turbulence based on the two-fluid drift-reduced Braginskii model. Resistivity-conditioned Koopma…
- Gray‐Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural OperatorTengfei Luo, Zhijie Li, Jianchun Wang, Pak Wai Chan et al. · Geophysical Research Letters · Aug 4, 2026
Abstract Previous machine learning (ML) weather prediction models primarily focus on global mesoscale forecasting. This study develops three‐dimensional Fourier neural operator (FNO) models for simulating the dry convective boundary layer (…
- Seismic wavefield solutions via physics-guided generative neural operatorWenhao Lv, Qizhen Du, Tariq Alkhalifah · Geophysics · Aug 2, 2026
Abstract Current neural operators often struggle to generalize to complex, out-of-distribution conditions, limiting their ability in seismic wavefield representation. To address this, we propose a generative neural operator (GNO) that lever…
- Koopman Neural Operator-based surrogate modelling for high-frequency dynamics of high-speed rail pantographsXufan Wang, Yang Song, Haonan Yang, Jingchuan Yao et al. · Mechanical Systems and Sign... · Aug 1, 2026
- Enhancing Fourier Neural Operators with Local Spatial FeaturesChaoyu Liu, Davide Murari, Lihao Liu, Yangming Li et al. · SIAM Journal on Scientific ... · Jul 31, 2026
Abstract. Partial differential equation (PDE) problems often exhibit strong local spatial structures, and accurately capturing these features is essential for high-quality solution approximation. The Fourier neural operator (FNO) has recent…
- How Important Are the Critical Points in Selecting the Optimal Samples for Accurate Estimation of Subsurface Soil Moisture?Vidhi Singh, Abhilash Singh, Kumar Gaurav · Geophysical Research Letters · Jul 30, 2026
Abstract We propose a novel physics‐aware sampling framework to extract the most informative training instances (critical points) from the conventional 70% training data set using six diversified sampling strategies. These critical points a…
- Rapid prediction of wind field in street canyons with infrastructure elements using neural operatorsGuozhu Liang, Ruikun Ge, Dongjin Cui, Jian Hang et al. · Urban Climate · Jul 29, 2026
- Will Neural Operators Replace Numerical Solvers?Aman Chourasia · OpenAlex · Jul 25, 2026
- Operator learning in nonlinear optics beyond perturbation theoryEmmanuel Lorin, Adam Miles, Xu Yang, Kemal Yazlyyev · Physica Scripta · Jul 21, 2026
Abstract This paper is focused on the computation of the nonperturbative nonlinear response of a gas to external intense electromagnetic fields. Ultimately, the long-term objective is to derive data-driven algorithms for the efficient compu…
- Transformer-based neural operators for 3D wind field prediction over complex mountainous terrainYujia Zhang, Jiaxi Qi, Ruiyan Chen, Ying Liu et al. · Communications Physics · Jul 20, 2026
- Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimationYuan Qiu, Wolfgang Dahmen, Peng Chen · Computer Methods in Applied... · Jul 17, 2026
- Graph Neural Operator-Based Surrogate Modelling of Multi-Field CFD Results in Biomass BoilerPrzemysław Motyl, Danuta Król, Sławomir Poskrobko · Energies · Jul 14, 2026
Computational fluid dynamics provides detailed spatial distributions of physical fields in biomass boiler combustion, but the computational cost of each simulation limits its application in parametric studies and near-real-time workflows. T…
- Neural Schur Complement Operators for Coarse-to-Fine Neural Operator TransferAnsh Tiwari · AI4Physics · May 30, 2026
Neural operators are advertised as discretization-flexible, but deploying them from a coarse grid to a finer one routinely loses accuracy that same-grid validation cannot predict. We show that this failure is structural rather than a traini…
- Hyperbolic Neural OperatorJieyuan Pei, Zhuoxuan Li, Wei Li, Haobo Zhang et al. · ICML 2026 regular · Apr 30, 2026
Neural operators have emerged as powerful surrogates for solving PDEs, significantly accelerating scientific computation. While transformer-based architectures offer unmatched flexibility for irregular domains, they suffer from a fundament…
- Physics-Informed Shearlet Neural Operator (PI-ShearletNO) for parametric partial differential equationsFabio Pereira dos Santos, Júlio de Castro Vargas Fernandes, Adriano M A Cortes · AI&PDE Poster · Mar 1, 2026
This paper introduces the Physics-Informed Shearlet Neural Operator (PI-ShearletNO), a framework for learning solution operators of parametric partial differential equations. The model combines neural operator learning with the geometric se…
- KANO: Kolmogorov-Arnold Neural OperatorJin Lee, Ziming Liu, Xinling Yu, Yixuan Wang et al. · ICLR 2026 Poster · Jan 26, 2026
We introduce Kolmogorov–Arnold Neural Operator (KANO), a dual‑domain neural operator jointly parameterized by both spectral and spatial bases with intrinsic symbolic interpretability. We theoretically demonstrate that KANO overcomes the pur…
- Tucker-FNO: Tensor Tucker-Fourier Neural Operator and its Universal Approximation TheoryGuancheng Zhou, Zelin Zeng, Yisi Luo, Qi Xie et al. · ICLR 2026 Poster · Jan 26, 2026
Fourier neural operator (FNO) has demonstrated substantial potential in learning mappings between function spaces, such as numerical partial differential equations (PDEs). However, FNO may suffer from inefficiencies when applied to large-sc…
- Generalized Koopman Neural Operator for Data-Driven Modeling of Electric Railway Pantograph–Catenary SystemsHui Wang, Yang Song, Haonan Yang, Zhigang Liu · IEEE Transactions on Transportation Electrification · Dec 1, 2025
In electric railways, the interaction performance of the pantograph–catenary systems (PCS) is crucial for maintaining a stable current supply. Establishing high-fidelity numerical models based on the finite-element method (FEM) is a common …
- NOWS: Neural Operator Warm Starts for Accelerating Iterative SolversM. Eshaghi, C. Anitescu, N. Valizadeh, Yizheng Wang et al. · Computer Methods in Applied Mechanics and Engineering · Nov 4, 2025
Partial differential equations (PDEs) underpin quantitative descriptions across the physical sciences and engineering, yet high-fidelity simulation remains a major computational bottleneck for many-query, real-time, and design tasks. Data-d…