Cover Image

Enhancing Convergence Speed with Feature-Enforcing Physics-Informed Neural Networks: Utilizing Boundary Conditions as Prior Knowledge for Faster Convergence

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jahaninasab, Mahyar, Bijarchi, Mohamad Ali
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Revising the Structure of Recurrent Neural Networks to Eliminate Numerical Derivatives in Forming Physics Informed Loss Terms with Respect to Time
by: Jahani-nasab, Mahyar, et al.
Published: (2024)

PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks
by: Sobh, Nahil, et al.
Published: (2025)

On Tuning Neural ODE for Stability, Consistency and Faster Convergence
by: Akhtar, Sheikh Waqas
Published: (2023)

Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)

SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
by: Anticev, John, et al.
Published: (2024)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
by: Jin, Bangti, et al.
Published: (2025)

Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)

Enhancing Physics-Informed Neural Networks Through Feature Engineering
by: Fazliani, Shaghayegh, et al.
Published: (2025)

Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
by: Graca, Manuel, et al.
Published: (2026)

DIM: Enforcing Domain-Informed Monotonicity in Deep Neural Networks
by: Salim, Joshua, et al.
Published: (2025)

Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2025)

Drop-Muon: Update Less, Converge Faster
by: Gruntkowska, Kaja, et al.
Published: (2025)

Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)
by: Zhang, Tianbao
Published: (2026)

Faster Convergence of Local SGD for Over-Parameterized Models
by: Qin, Tiancheng, et al.
Published: (2022)

Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework
by: Hassanzadeh, Meraj, et al.
Published: (2025)

A Novel Method for Enforcing Exactly Dirichlet, Neumann and Robin Conditions on Curved Domain Boundaries for Physics Informed Machine Learning
by: Dong, Suchuan, et al.
Published: (2026)

FlexPINN: Modeling Fluid Dynamics and Mass Transfer in 3D Micromixer Geometries Using a Flexible Physics-Informed Neural Network
by: Hassanzadeh, Meraj, et al.
Published: (2025)

Fourier Feature Pyramids for Physics-Informed Neural Networks
by: Zhao, Brandon, et al.
Published: (2026)

Optimal Convergence Rates of Deep Neural Network Classifiers
by: Zhang, Zihan, et al.
Published: (2025)

Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation
by: Mishkin, Aaron, et al.
Published: (2024)

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
by: Liu, Zhuanghua, et al.
Published: (2024)

Faster Convergence for Transformer Fine-tuning with Line Search Methods
by: Kenneweg, Philip, et al.
Published: (2024)

Enforcing convex constraints in Graph Neural Networks
by: Rashwan, Ahmed, et al.
Published: (2025)

Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
by: Ahmed, Mohammed Zain Ali
Published: (2026)

Fairness-aware Federated Minimax Optimization with Convergence Guarantee
by: Dunda, Gerry Windiarto Mohamad, et al.
Published: (2023)

On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
by: Li, Chenyang, et al.
Published: (2023)

Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
by: Buskulic, Nathan, et al.
Published: (2023)

Benchmarking Physics-Informed Neural Networks and Boundary Elements Methods for Wave Scattering
by: Rincón-Cardeno, Oscar, et al.
Published: (2025)

Bayesian Neural Networks with Domain Knowledge Priors
by: Sam, Dylan, et al.
Published: (2024)

Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
by: Oowada, Kanata, et al.
Published: (2025)

VAMO: Efficient Zeroth-Order Variance Reduction for SGD with Faster Convergence
by: Chen, Jiahe, et al.
Published: (2025)

Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition
by: Tepakbong, Nathanael, et al.
Published: (2025)

Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach
by: Hu, Gang, et al.
Published: (2024)

Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence
by: Deng, Yichuan, et al.
Published: (2024)

Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
by: Caron, Francois, et al.
Published: (2023)

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit
by: Riedl, Konstantin, et al.
Published: (2026)

Optimal Convergence Rates for Neural Operators
by: Nguyen, Mike, et al.
Published: (2024)

Iterative Training of Physics-Informed Neural Networks with Fourier-enhanced Features
by: Wu, Yulun, et al.
Published: (2025)

Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
by: Fu, Guoji, et al.
Published: (2023)

Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks
by: Lastrucci, Giacomo, et al.
Published: (2025)