Copertina

Solving the Fisher nonlinear differential equations via Physics-Informed Neural Networks: A Comprehensive Retraining Study and Comparative Analysis with the Finite Difference Method

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Aberqi, Ahmed, Miloudi, Ahmed
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Numerical Analysis 65L12, 35G20, 68T07
Accesso online:	Acceder al recurso
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)

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)

Learning WENO for entropy stable schemes to solve conservation laws
di: Charles, Philip, 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)

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

A Variational Physics-Informed Neural Network Framework Using Petrov-Galerkin Method for Solving Singularly Perturbed Boundary Value Problems
di: Kumar, Vijay, et al.
Pubblicazione: (2025)

Least Squares with Equality constraints Extreme Learning Machines for the resolution of PDEs
di: De Falco, Davide Elia, et al.
Pubblicazione: (2025)

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

Physics-Informed Neural Networks: Bridging the Divide Between Conservative and Non-Conservative Equations
di: Neelan, Arun Govind, et al.
Pubblicazione: (2025)

Convergent Operator-Splitting Scheme for Viscosity Solutions: A Foundation for Learning Domain-to-Solution Maps
di: Wu, Po-Yi
Pubblicazione: (2025)

Development and Analysis of Chien-Physics-Informed Neural Networks for Singular Perturbation Problems
di: Singh, Gautam, et al.
Pubblicazione: (2025)

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

Convergence of Physics-Informed Neural Networks for Fully Nonlinear PDE's
di: Arakelyan, Avetik, et al.
Pubblicazione: (2024)

Learn and Verify: A Framework for Rigorous Verification of Physics-Informed Neural Networks
di: Tanaka, Kazuaki, et al.
Pubblicazione: (2026)

A Stabilized Physics Informed Neural Networks Method for Wave Equations
di: Jiao, Yuling, et al.
Pubblicazione: (2024)

Neural Network Dual Norms for Minimal Residual Finite Element Methods
di: Alsobhi, Hamd, et al.
Pubblicazione: (2025)

Solving the BGK Model and Boltzmann equation by Fourier Neural Operator with conservative constraints
di: Hu, Boyun, et al.
Pubblicazione: (2025)

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

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

Neural Network Element Method for Partial Differential Equations
di: Wang, Yifan, et al.
Pubblicazione: (2025)

Solving High Dimensional Partial Differential Equations Using Tensor Neural Network and A Posteriori Error Estimators
di: Wang, Yifan, et al.
Pubblicazione: (2023)

Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems
di: Caldana, Matteo, et al.
Pubblicazione: (2024)

Regularity Analysis and Tensor Neural Network Methods for Quasiperiodic Elliptic Equations
di: Ren, Jingze, et al.
Pubblicazione: (2026)

Solving the inverse source problem of the fractional Poisson equation by MC-fPINNs
di: Sheng, Rui, et al.
Pubblicazione: (2024)

Prediction of discretization of online GMsFEM using deep learning for Richards equation
di: Spiridonov, Denis, et al.
Pubblicazione: (2024)

Event-Level Probabilistic Prediction of Extreme Rainfall over India Using Physics-Gated Latent Dynamics
di: Neelan, Arun Govind
Pubblicazione: (2026)

Linear Stability Analysis of Physics-Informed Random Projection Neural Networks for ODEs
di: Fabiani, Gianluca, et al.
Pubblicazione: (2024)

Capturing Shock Waves by Relaxation Neural Networks
di: Zhou, Nan, et al.
Pubblicazione: (2024)

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

Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations
di: Sommer, Finn, et al.
Pubblicazione: (2026)

RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators
di: Fabiani, Gianluca, et al.
Pubblicazione: (2024)

Solving nonlinear Klein-Gordon equation with non-smooth solution by a geometric low-regularity integrator
di: Wang, Bin, et al.
Pubblicazione: (2023)

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

The lowest-order Neural Approximated Virtual Element Method on polygonal elements
di: Berrone, Stefano, et al.
Pubblicazione: (2024)

Semi-implicit-explicit Runge-Kutta method for nonlinear differential equations
di: Ding, Lingyun
Pubblicazione: (2025)

EGPT-PINN: Entropy-enhanced Generative Pre-Trained Physics Informed Neural Networks for parameterized nonlinear conservation laws
di: Ji, Yajie, et al.
Pubblicazione: (2025)

A Second-Order Maximum-Principle-Preserving Crouzeix-Raviart Finite Element Method for Time-dependent Transport Equation
di: Mao, Shipeng, et al.
Pubblicazione: (2026)

Convergence Guarantees for Neural Network-Based Hamilton-Jacobi Reachability
di: Hofgard, William
Pubblicazione: (2024)

Solving Hamilton-Jacobi equations by minimizing residuals of monotone discretizations
di: Bokanowski, Olivier, et al.
Pubblicazione: (2026)

Numerical solutions of ordinary differential equations using Spline-Integral Operator
di: Salgado, Gustavo H. O., et al.
Pubblicazione: (2024)