Copertina

Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Florido, Jose, Wang, He, Khan, Amirul, Jimack, Peter K.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Hard Constraint Projection in a Physics Informed Neural Network
di: Horne, Miranda J. S., et al.
Pubblicazione: (2026)

Generative modeling of Sparse Approximate Inverse Preconditioners
di: Li, Mou, et al.
Pubblicazione: (2024)

Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks
di: Caradot, Antoine, et al.
Pubblicazione: (2025)

RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
di: Song, Zhenao
Pubblicazione: (2025)

PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
di: Bonfanti, Andrea, et al.
Pubblicazione: (2025)

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
di: Roy, Sumanta, et al.
Pubblicazione: (2024)

Auto-Adaptive PINNs with Applications to Phase Transitions
di: Buck, Kevin, et al.
Pubblicazione: (2025)

Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases
di: Cooley, Madison, et al.
Pubblicazione: (2024)

Gradient Scaling Effects in Adaptive Spectral PINNs for Stiff Nonlinear ODEs
di: Yepes, Isabela M., et al.
Pubblicazione: (2026)

GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
di: Jiao, Yuling, et al.
Pubblicazione: (2023)

PACMANN: Point Adaptive Collocation Method for Artificial Neural Networks
di: Visser, Coen, et al.
Pubblicazione: (2024)

Physics-Guided Transformer (PGT): Physics-Aware Attention Mechanism for PINNs
di: Zeraatkar, Ehsan, et al.
Pubblicazione: (2026)

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

PINNs Failure Modes are Overfitting
di: Andersen, Nigel T., et al.
Pubblicazione: (2026)

AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
di: Botvinick-Greenhouse, Jonah, et al.
Pubblicazione: (2025)

An Adaptive Importance Sampling for Locally Stable Point Processes
di: Kang, Hee-Geon, et al.
Pubblicazione: (2024)

Advancing Generalization in PINNs through Latent-Space Representations
di: Wang, Honghui, et al.
Pubblicazione: (2024)

A Control Perspective on Training PINNs
di: Barreau, Matthieu, et al.
Pubblicazione: (2025)

Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
di: Celaya, Adrian, et al.
Pubblicazione: (2025)

Mitigating Gradient Pathology in PINNs through Aligned Constraint
di: Luo, Yichen, et al.
Pubblicazione: (2026)

When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
di: Cao, Yuan-dong, et al.
Pubblicazione: (2026)

MILP initialization for solving parabolic PDEs with PINNs
di: Li, Sirui, et al.
Pubblicazione: (2025)

Understanding the Difficulty of Solving Cauchy Problems with PINNs
di: Wang, Tao, et al.
Pubblicazione: (2024)

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
di: Kumar, Dibyakanti, et al.
Pubblicazione: (2023)

Learning in PINNs: Phase transition, total diffusion, and generalization
di: Anagnostopoulos, Sokratis J., et al.
Pubblicazione: (2024)

Integral regularization PINNs for evolution equations
di: Feng, Xiaodong, et al.
Pubblicazione: (2025)

When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions
di: Wang, Sifan, et al.
Pubblicazione: (2026)

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
di: Trestman, Morris, et al.
Pubblicazione: (2025)

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs
di: Huo, Wenxuan, et al.
Pubblicazione: (2025)

SetPINNs: Set-based Physics-informed Neural Networks
di: Nagda, Mayank, et al.
Pubblicazione: (2024)

Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
di: Babić, Miloš, et al.
Pubblicazione: (2026)

Bayesian PINNs for uncertainty-aware inverse problems (BPINN-IP)
di: Mohammad-Djafari, Ali
Pubblicazione: (2026)

$Δ$-PINNs: physics-informed neural networks on complex geometries
di: Costabal, Francisco Sahli, et al.
Pubblicazione: (2022)

PINNs Algorithmic Framework for Simulation of Nonlinear Burgers' Type Models
di: Singh, Ajeet, et al.
Pubblicazione: (2025)

Noisy PDE Training Requires Bigger PINNs
di: Andre-Sloan, Sebastien, et al.
Pubblicazione: (2025)

An Evaluation of Real-time Adaptive Sampling Change Point Detection Algorithm using KCUSUM
di: Saravanan, Vijayalakshmi, et al.
Pubblicazione: (2024)

Adaptively Point-weighting Curriculum Learning
di: Li, Wensheng, et al.
Pubblicazione: (2025)

Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets
di: Salloum, Hadi, et al.
Pubblicazione: (2026)

AdapSNE: Adaptive Fireworks-Optimized and Entropy-Guided Dataset Sampling for Edge DNN Training
di: Zhao, Boran, et al.
Pubblicazione: (2025)

RRaPINNs: Residual Risk-Aware Physics Informed Neural Networks
di: Akazan, Ange-Clément, et al.
Pubblicazione: (2025)