Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Mattey, Revanth, Ghosh, Susanta |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
by: Chen, Yifan, et al.
Published: (2023)
by: Chen, Yifan, et al.
Published: (2023)
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026)
by: Feng, Xue, et al.
Published: (2026)
DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
by: Huang, Chih-Kang, et al.
Published: (2026)
by: Huang, Chih-Kang, et al.
Published: (2026)
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
by: Ghosh, Shinjan, et al.
Published: (2024)
by: Ghosh, Shinjan, et al.
Published: (2024)
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
by: Fang, Zhiwei, et al.
Published: (2023)
by: Fang, Zhiwei, et al.
Published: (2023)
Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
by: Jin, Bangti, et al.
Published: (2025)
by: Jin, Bangti, et al.
Published: (2025)
Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers
by: Trifonov, Vladislav, et al.
Published: (2024)
by: Trifonov, Vladislav, et al.
Published: (2024)
Point Source Identification Using Singularity Enriched Neural Networks
by: Hu, Tianhao, et al.
Published: (2024)
by: Hu, Tianhao, et al.
Published: (2024)
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks
by: Jacob, Bruno, et al.
Published: (2024)
by: Jacob, Bruno, et al.
Published: (2024)
Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models
by: Saporiti, Riccardo, et al.
Published: (2026)
by: Saporiti, Riccardo, et al.
Published: (2026)
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
by: Norman, Grant, et al.
Published: (2024)
by: Norman, Grant, et al.
Published: (2024)
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
by: Hwang, Youngsik, et al.
Published: (2024)
by: Hwang, Youngsik, et al.
Published: (2024)
An End-to-End Deep Learning Method for Solving Nonlocal Allen-Cahn and Cahn-Hilliard Phase-Field Models
by: Geng, Yuwei, et al.
Published: (2024)
by: Geng, Yuwei, et al.
Published: (2024)
Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
by: Park, Yesom, et al.
Published: (2025)
by: Park, Yesom, et al.
Published: (2025)
Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
by: Chen, Chuqi, et al.
Published: (2024)
by: Chen, Chuqi, et al.
Published: (2024)
Neural Conjugate Flows: Physics-informed architectures with flow structure
by: Bizzi, Arthur, et al.
Published: (2024)
by: Bizzi, Arthur, et al.
Published: (2024)
Radial Müntz-Szász Networks: Neural Architectures with Learnable Power Bases for Multidimensional Singularities
by: N'guessan, Gnankan Landry Regis, et al.
Published: (2026)
by: N'guessan, Gnankan Landry Regis, et al.
Published: (2026)
A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
by: Chen, Zhenglong, et al.
Published: (2025)
by: Chen, Zhenglong, et al.
Published: (2025)
Extended Physics Informed Neural Network for Hyperbolic Two-Phase Flow in Porous Media
by: Rehman, Saif Ur, et al.
Published: (2025)
by: Rehman, Saif Ur, et al.
Published: (2025)
Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators
by: Pellegrini, Luca, et al.
Published: (2025)
by: Pellegrini, Luca, et al.
Published: (2025)
Multigrade Neural Network Approximation
by: Zhang, Shijun, et al.
Published: (2026)
by: Zhang, Shijun, et al.
Published: (2026)
First-order PDES for Graph Neural Networks: Advection And Burgers Equation Models
by: Qu, Yifan, et al.
Published: (2024)
by: Qu, Yifan, et al.
Published: (2024)
Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)
by: Liu, Songming, et al.
Published: (2024)
THINNs: Thermodynamically Informed Neural Networks
by: Castro, Javier, et al.
Published: (2025)
by: Castro, Javier, et al.
Published: (2025)
Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations
by: Alipourfard, Borzou, et al.
Published: (2019)
by: Alipourfard, Borzou, et al.
Published: (2019)
Sampling via Gradient Flows in the Space of Probability Measures
by: Chen, Yifan, et al.
Published: (2023)
by: Chen, Yifan, et al.
Published: (2023)
Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators
by: Herb, Julius, et al.
Published: (2025)
by: Herb, Julius, et al.
Published: (2025)
Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)
by: Zhou, Chenhong, et al.
Published: (2025)
Estimating condition number with Graph Neural Networks
by: Carson, Erin, et al.
Published: (2026)
by: Carson, Erin, et al.
Published: (2026)
Regularized Stein Variational Gradient Flow
by: He, Ye, et al.
Published: (2022)
by: He, Ye, et al.
Published: (2022)
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
by: Yang, Jianan, et al.
Published: (2026)
by: Yang, Jianan, et al.
Published: (2026)
Decentralized Neural Networks for Robust and Scalable Eigenvalue Computation
by: Katende, Ronald
Published: (2024)
by: Katende, Ronald
Published: (2024)
Parallel-in-Time Solutions with Random Projection Neural Networks
by: Betcke, Marta M., et al.
Published: (2024)
by: Betcke, Marta M., et al.
Published: (2024)
E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)
by: Jacob, Bruno, et al.
Published: (2025)
Deep NURBS -- Admissible Physics-informed Neural Networks
by: Saidaoui, Hamed, et al.
Published: (2022)
by: Saidaoui, Hamed, et al.
Published: (2022)
Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties
by: Mohanty, Shaswat, et al.
Published: (2024)
by: Mohanty, Shaswat, et al.
Published: (2024)
Deep Neural Network Solutions for Oscillatory Fredholm Integral Equations
by: Jiang, Jie, et al.
Published: (2024)
by: Jiang, Jie, et al.
Published: (2024)
Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)
by: Xu, Qingsong, et al.
Published: (2024)
Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
by: Guan, Jiajing, et al.
Published: (2024)
by: Guan, Jiajing, et al.
Published: (2024)
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
by: Chen, Jiale, et al.
Published: (2024)
by: Chen, Jiale, et al.
Published: (2024)
Similar Items
-
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
by: Chen, Yifan, et al.
Published: (2023) -
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026) -
DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
by: Huang, Chih-Kang, et al.
Published: (2026) -
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
by: Ghosh, Shinjan, et al.
Published: (2024) -
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
by: Fang, Zhiwei, et al.
Published: (2023)