:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Huang, Xujia, Wang, Fajie, Zhang, Benrong, Liu, Hanqing
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Computational Physics 65M99, 35M33, 68T07
Accesso online:	https://arxiv.org/abs/2402.01768
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
di: Chen, Gaohang, et al.
Pubblicazione: (2025)

Learning on the Temporal Tangent Bundle for Physics-Informed Neural Networks
di: Jamal, Adetola, et al.
Pubblicazione: (2026)

ARDO: A Weak Formulation Deep Neural Network Method for Elliptic and Parabolic PDEs Based on Random Differences of Test Functions
di: Cai, Wei, et al.
Pubblicazione: (2025)

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
di: Park, Youngjae, et al.
Pubblicazione: (2026)

Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle
di: Tahimi, Abdeladhim
Pubblicazione: (2026)

S$^2$GPT-PINNs: Sparse and Small models for PDEs
di: Ji, Yajie, et al.
Pubblicazione: (2025)

Efficient kernel surrogates for neural network-based regression
di: Qadeer, Saad, et al.
Pubblicazione: (2023)

PODNO: Proper Orthogonal Decomposition Neural Operators
di: Cheng, Zilan, et al.
Pubblicazione: (2025)

Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations
di: Witte, Maximilian, et al.
Pubblicazione: (2024)

Neural-HSS: Hierarchical Semi-Separable Neural PDE Solver
di: Sittoni, Pietro, et al.
Pubblicazione: (2026)

Neural Networks for Threshold Dynamics Reconstruction
di: Negrini, Elisa, et al.
Pubblicazione: (2024)

Error bounds for Physics Informed Neural Networks in Generalized KdV Equations placed on unbounded domains
di: Freire, Ricardo, et al.
Pubblicazione: (2026)

A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks
di: Ko, Gyounghun, et al.
Pubblicazione: (2026)

Enhancing Future Prediction of Linear and Nonlinear Reduced-Order Models for Transport-Dominated Problems Using Lagrangian Data
di: Li, Meng, et al.
Pubblicazione: (2026)

GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2024)

Adaptive Domain Decomposition Physics-Informed Neural Networks for Traffic State Estimation with Sparse Sensor Data
di: Ka, Eunhan, et al.
Pubblicazione: (2026)

Convex Physics Informed Neural Networks for the Monge-Ampère Optimal Transport Problem
di: Caboussat, Alexandre, et al.
Pubblicazione: (2025)

Error bounds for Physics Informed Neural Networks in Nonlinear Schrödinger equations placed on unbounded domains
di: Alejo, Miguel Á., et al.
Pubblicazione: (2024)

Learning WENO for entropy stable schemes to solve conservation laws
di: Charles, Philip, et al.
Pubblicazione: (2024)

Intrinsic-Metric Physics-Informed Neural Networks (IM-PINN) for Reaction-Diffusion Dynamics on Complex Riemannian Manifolds
di: Chrisnanto, Julian Evan, et al.
Pubblicazione: (2025)

High-Fidelity Modeling of Stochastic Chemical Dynamics on Complex Manifolds: A Multi-Scale SIREN-PINN Framework for the Curvature-Perturbed Ginzburg-Landau Equation
di: Chrisnanto, Julian Evan, et al.
Pubblicazione: (2026)

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory
di: Zhang, Xifeng, et al.
Pubblicazione: (2026)

Deep Neural ODE Operator Networks for PDEs
di: Li, Ziqian, et al.
Pubblicazione: (2025)

All Equalities Are Equal, but Some Are More Equal Than Others: The Effect of Implementation Aliasing on the Numerical Solution to Conservation Equations
di: Trojak, Will, et al.
Pubblicazione: (2019)

Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck Equations
di: Wu, Xiaolong, et al.
Pubblicazione: (2025)

Autoencoders in Function Space
di: Bunker, Justin, et al.
Pubblicazione: (2024)

Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
di: Cooley, Madison, et al.
Pubblicazione: (2024)

A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2024)

Error Estimation for Physics-informed Neural Networks Approximating Semilinear Wave Equations
di: Lorenz, Beatrice, et al.
Pubblicazione: (2024)

Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks
di: Patsatzis, Dimitrios G., et al.
Pubblicazione: (2025)

A Parameter-Driven Physics-Informed Neural Network Framework for Solving Two-Parameter Singular Perturbation Problems Involving Boundary Layers
di: Boro, Pradanya, et al.
Pubblicazione: (2025)

PTL-PINNs: Perturbation-Guided Transfer Learning with Physics- Informed Neural Networks for Nonlinear Systems
di: Alexandrino, Duarte, et al.
Pubblicazione: (2026)

Tensor Neural Network Based Machine Learning Method for Elliptic Multiscale Problems
di: Lin, Zhongshuo, et al.
Pubblicazione: (2024)

A neural network method for scalar conservation laws with convergence rates for shock-wave solutions
di: Cao, Jiachuan, et al.
Pubblicazione: (2026)

RONOM: Reduced-Order Neural Operator Modeling
di: Dummer, Sven, et al.
Pubblicazione: (2025)

Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse
di: Chung, Seung Whan, et al.
Pubblicazione: (2025)

Hypernetwork-Conditioned WENO5 Conservative-Form CNNs for One-Dimensional Conservation Laws
di: Chen, Yongsheng, et al.
Pubblicazione: (2026)

PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems
di: Aghaei, Alireza Afzal, et al.
Pubblicazione: (2024)

Neural network Approximations for Reaction-Diffusion Equations -- Homogeneous Neumann Boundary Conditions and Long-time Integrations
di: Avilés, Eddel Elí Ojeda, et al.
Pubblicazione: (2024)

Structure-Preserving Discretization of Fractional Vector Calculus using Discrete Exterior Calculus
di: Jacobson, Alon, et al.
Pubblicazione: (2022)