Cover Image

Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Zhaohui, Yang, Shihao, Wu, Jeff
Format:	Preprint
Published:	2022
Subjects:	Numerical Analysis Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
by: Hao, Wenrui, et al.
Published: (2024)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
by: Mora, Carlos, et al.
Published: (2024)

Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation
by: Tong, Xin T., et al.
Published: (2026)

Numerically robust Gaussian state estimation with singular observation noise
by: Krämer, Nicholas, et al.
Published: (2025)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

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

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

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2024)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
by: Liang, Senwei, et al.
Published: (2022)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
by: Hardwick, Gareth, et al.
Published: (2024)

Kinetic Langevin Splitting Schemes for Constrained Sampling
by: Chada, Neil K., et al.
Published: (2026)

Regression-aware decompositions
by: Tygert, Mark
Published: (2017)

Multivariate Density Estimation via Variance-Reduced Sketching
by: Peng, Yifan, et al.
Published: (2024)

Variational Markov chain mixtures with automatic component selection
by: Miles, Christopher E., et al.
Published: (2024)

Parameter Estimation for Partially Observed McKean-Vlasov Diffusions
by: Jasra, Ajay, et al.
Published: (2024)

Unbiased Parameter Estimation of Partially Observed Diffusions using Diffusion Bridges
by: Alvarez, Miguel, et al.
Published: (2025)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
by: Mialon, Grégoire, et al.
Published: (2023)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
by: Sun, Jingbo, et al.
Published: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026)

Parameter Estimation for Partially Observed Time-Changed SDEs
by: Zhao, Ke, et al.
Published: (2026)

Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
by: Mahowald, Jamie, et al.
Published: (2026)

AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling
by: Mikuš, Iva, et al.
Published: (2026)

Median of Means Sampling for the Keister Function
by: Zhang, Bocheng
Published: (2025)

Tensor Decomposition with Unaligned Observations
by: Tang, Runshi, et al.
Published: (2024)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
by: Sun, Jingmin, et al.
Published: (2024)

Data-Driven Self-Supervised Learning for the Discovery of Solution Singularity for Partial Differential Equations
by: Cai, Difeng, et al.
Published: (2025)

Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
by: Negi, Pawan, et al.
Published: (2024)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
by: Hammad, M. M.
Published: (2026)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
by: Ye, Zaijun, et al.
Published: (2024)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)

Physics-Informed Inference Time Scaling for Solving High-Dimensional PDE via Defect Correction
by: Fan, Zexi, et al.
Published: (2025)

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
by: Yin, Pengsong, et al.
Published: (2024)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)

A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations
by: Aghaei, Alireza Afzal
Published: (2024)