Cover Image

Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Caradot, Antoine, Emonet, Rémi, Habrard, Amaury, Mezidi, Abdel-Rahim, Sebban, Marc
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Méthode de quadrature pour les PINNs fondée théoriquement sur la hessienne des résiduels
by: Caradot, Antoine, et al.
Published: (2025)

Unrolled-SINDy: A Stable Explicit Method for Non linear PDE Discovery from Sparsely Sampled Data
by: Banna, Fayad Ali, et al.
Published: (2025)

Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
by: Viallard, Paul, et al.
Published: (2024)

A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
by: Nguyen, Thien V., et al.
Published: (2026)

From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction
by: Marouani, Sami, et al.
Published: (2025)

Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs
by: Florido, Jose, et al.
Published: (2024)

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

On the Closed-Form of Flow Matching: Generalization Does Not Arise from Target Stochasticity
by: Bertrand, Quentin, et al.
Published: (2025)

Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
by: Singh, Kamal, et al.
Published: (2025)

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

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics
by: Wei, Amaury, et al.
Published: (2024)

CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs
by: Elshamy, Mohamed R., et al.
Published: (2025)

Recurrent Neural Networks for Dynamic VWAP Execution: Adaptive Trading Strategies with Temporal Kolmogorov-Arnold Networks
by: Genet, Remi
Published: (2025)

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

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression
by: Chowdhury, Agniva, et al.
Published: (2024)

Adaptive Physics-informed Neural Networks: A Survey
by: Torres, Edgar, et al.
Published: (2025)

Provably Accurate Shapley Value Estimation via Leverage Score Sampling
by: Musco, Christopher, et al.
Published: (2024)

PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers
by: Bastian, Julien, et al.
Published: (2026)

Floating-Point Neural Networks Are Provably Robust Universal Approximators
by: Hwang, Geonho, et al.
Published: (2025)

Stabilized Adaptive Loss and Residual-Based Collocation for Physics-Informed Neural Networks
by: Singh, Divyavardhan, et al.
Published: (2026)

Inner Product Aware Quantization: Provably Fast, Accurate, and Adaptive Algorithms
by: White, Nathan, et al.
Published: (2026)

MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
by: Modoranu, Ionut-Vlad, et al.
Published: (2024)

Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout
by: Safder, Adeel
Published: (2025)

Provable Imbalanced Point Clustering
by: Denisov, David, et al.
Published: (2024)

Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
by: Bu, Dake, et al.
Published: (2024)

An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
by: Luo, Jiaqi, et al.
Published: (2025)

Adaptive Sampling for Continuous Group Equivariant Neural Networks
by: Inal, Berfin, et al.
Published: (2024)

Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
by: Wang, Shuai, et al.
Published: (2024)

Provably Bounding Neural Network Preimages
by: Kotha, Suhas, et al.
Published: (2023)

Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
by: Yang, Yu, et al.
Published: (2023)

Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality
by: Pacifico, Feliciano Giuseppe, et al.
Published: (2026)

PDE-aware Optimizer for Physics-informed Neural Networks
by: Churiwala, Vismay, et al.
Published: (2025)

Physics-informed Neural Networks with Unknown Measurement Noise
by: Pilar, Philipp, et al.
Published: (2022)

Adaptive Physics-Guided Neural Network
by: Shulman, David, et al.
Published: (2024)

SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms
by: Genet, Rémi, et al.
Published: (2025)

Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval
by: Le, Hang Thi-Thuy, et al.
Published: (2026)

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

Provable Privacy Attacks on Trained Shallow Neural Networks
by: Smorodinsky, Guy, et al.
Published: (2024)

Provably Powerful Graph Neural Networks for Directed Multigraphs
by: Egressy, Béni, et al.
Published: (2023)

Block Coordinate Descent for Neural Networks Provably Finds Global Minima
by: Akiyama, Shunta
Published: (2025)