Imagen de Portada

DiffGrad for Physics-Informed Neural Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Rahman, Jamshaid Ul, Nimra
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

SwishReLU: A Unified Approach to Activation Functions for Enhanced Deep Neural Networks Performance
por: Rahman, Jamshaid Ul, et al.
Publicado: (2024)

Evidential Physics-Informed Neural Networks
por: Tan, Hai Siong, et al.
Publicado: (2025)

GradINN: Gradient Informed Neural Network
por: Aglietti, Filippo, et al.
Publicado: (2024)

Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks
por: Naujoks, Jonas R., et al.
Publicado: (2025)

Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges
por: Shajihan, Althaf, et al.
Publicado: (2025)

Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
por: Urbán, Jorge F., et al.
Publicado: (2024)

Supervised Metric Regularization Through Alternating Optimization for Multi-Regime Physics-Informed Neural Networks
por: Spotorno, Enzo Nicolas, et al.
Publicado: (2026)

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
por: Flores, Pablo, et al.
Publicado: (2025)

PINNfluence: Influence Functions for Physics-Informed Neural Networks
por: Naujoks, Jonas R., et al.
Publicado: (2024)

Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeam
por: Garai, Ramanath, et al.
Publicado: (2026)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
por: Mazandarani, Mehran, et al.
Publicado: (2025)

Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
por: Garban, Manuel Ricardo Guevara, et al.
Publicado: (2025)

Empirical Stability Analysis of Kolmogorov-Arnold Networks in Hard-Constrained Recurrent Physics-Informed Discovery
por: Spotorno, Enzo Nicolas, et al.
Publicado: (2026)

Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
por: Chiu, Pao-Hsiung, et al.
Publicado: (2026)

Calibrated Physics-Informed Uncertainty Quantification
por: Gopakumar, Vignesh, et al.
Publicado: (2025)

Spectral Informed Neural Network: An Efficient and Low-Memory PINN
por: Yu, Tianchi, et al.
Publicado: (2024)

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
por: Kong, Chun-Wei, et al.
Publicado: (2024)

Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
por: Wang, Sifan, et al.
Publicado: (2025)

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
por: Toscano, Juan Diego, et al.
Publicado: (2024)

Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask
por: Es'kin, Vasiliy A., et al.
Publicado: (2026)

Physics-Informed Neural Networks and Extensions
por: Raissi, Maziar, et al.
Publicado: (2024)

Complex Physics-Informed Neural Network
por: Si, Chenhao, et al.
Publicado: (2025)

Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
por: Wang, Sifan, et al.
Publicado: (2025)

Evaluation and Verification of Physics-Informed Neural Models of the Grad-Shafranov Equation
por: Rizqan, Fauzan Nazranda, et al.
Publicado: (2025)

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
por: Li, Dong, et al.
Publicado: (2026)

TextGrad: Automatic "Differentiation" via Text
por: Yuksekgonul, Mert, et al.
Publicado: (2024)

Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML
por: Viswanath, Hrishikesh, et al.
Publicado: (2023)

Feature Mapping in Physics-Informed Neural Networks (PINNs)
por: Zeng, Chengxi, et al.
Publicado: (2024)

Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
por: Jiang, Feilong, et al.
Publicado: (2024)

Densely Multiplied Physics Informed Neural Networks
por: Jiang, Feilong, et al.
Publicado: (2024)

Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
por: Na, Gyoung S., et al.
Publicado: (2026)

DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation
por: Franceschelli, Giorgio, et al.
Publicado: (2025)

Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation
por: Bora, Aniruddha, et al.
Publicado: (2026)

GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning
por: Yang, Ningyuan, et al.
Publicado: (2026)

Physics-Informed Graph Neural Networks for Water Distribution Systems
por: Ashraf, Inaam, et al.
Publicado: (2024)

Improved Training of Physics-Informed Neural Networks with Model Ensembles
por: Haitsiukevich, Katsiaryna, et al.
Publicado: (2022)

Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
por: An, Kang, et al.
Publicado: (2026)

AlphaGrad: Non-Linear Gradient Normalization Optimizer
por: Sane, Soham
Publicado: (2025)

GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks
por: Sanderson, Jacob, et al.
Publicado: (2026)

Hybrid Quantum-Classical Recurrent Neural Networks
por: Xu, Wenduan
Publicado: (2025)