Cover Image

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kim, Heechang, Cao, Qianying, Shin, Hyomin, Lee, Seungchul, Karniadakis, George Em, Choi, Minseok
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
by: Shih, Benjamin, et al.
Published: (2024)

Physics-Informed Machine Learning in Biomedical Science and Engineering
by: Ahmadi, Nazanin, et al.
Published: (2025)

WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations
by: Abid, Muhammad, et al.
Published: (2026)

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
by: Gao, Zhiwei, et al.
Published: (2025)

Two-scale Neural Networks for Partial Differential Equations with Small Parameters
by: Zhuang, Qiao, et al.
Published: (2024)

Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)

L-HYDRA: Multi-Head Physics-Informed Neural Networks
by: Zou, Zongren, et al.
Published: (2023)

Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations
by: Hu, Zheyuan, et al.
Published: (2024)

A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoring
by: Nath, Kamaljyoti, et al.
Published: (2024)

Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
by: Khodakarami, Siavash, et al.
Published: (2025)

Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
by: Peyvan, Ahmad, et al.
Published: (2025)

Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
by: Zhang, Qian, et al.
Published: (2026)

Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Levy Equations
by: Hu, Zheyuan, et al.
Published: (2024)

Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
by: Michałowska, Katarzyna, et al.
Published: (2023)

UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning
by: Qiao, Hanli, et al.
Published: (2026)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
by: Kim, Chanyoung, et al.
Published: (2026)

Spectral Audit of In-Context Operator Networks
by: Gao, Zhiwei, et al.
Published: (2026)

RiemannONets: Interpretable Neural Operators for Riemann Problems
by: Peyvan, Ahmad, et al.
Published: (2024)

GMC-PINNs: A new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains
by: Wang, Shupeng, et al.
Published: (2024)

Automatic selection of the best neural architecture for time series forecasting
by: Cao, Qianying, et al.
Published: (2025)

Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)

Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)

Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
by: Oommen, Vivek, et al.
Published: (2024)

Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks
by: Kiyani, Elham, et al.
Published: (2025)

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2024)

Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
by: Chen, Biao, et al.
Published: (2026)

Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
by: Farea, Afrah, et al.
Published: (2024)

Agentic Risk-Aware Set-Based Engineering Design
by: Kumar, Varun, et al.
Published: (2026)

Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method
by: Kopaničáková, Alena, et al.
Published: (2025)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)

A Neural-Operator Preconditioned Newton Method for Accelerated Nonlinear Solvers
by: Lee, Youngkyu, et al.
Published: (2025)

Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
by: Kumar, Varun, et al.
Published: (2025)

Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
by: Shin, Jin Young, et al.
Published: (2022)

Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
by: Lee, Youngkyu, et al.
Published: (2025)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
by: Yang, Yahong
Published: (2024)

Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
by: Katende, Ronald
Published: (2025)

Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
by: Gupta, Aarush, et al.
Published: (2025)

PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
by: Matthews, Jason, et al.
Published: (2024)