Cover Image

Auto-Adaptive PINNs with Applications to Phase Transitions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Buck, Kevin, Kim, Woojeong
Format:	Preprint
Published:	2025
Subjects:	Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Convergence Properties of PINNs for the Navier-Stokes-Cahn-Hilliard System
by: Buck, Kevin, et al.
Published: (2025)

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

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

E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)

Multi-Preconditioned LBFGS for Training Finite-Basis PINNs
by: Salvadó-Benasco, Marc, et al.
Published: (2026)

Accelerating Fractional PINNs using Operational Matrices of Derivative
by: Taheri, Tayebeh, et al.
Published: (2024)

Local Well-Posedness of a Modified NSCH-Oldroyd System: PINN-Based Numerical Computation
by: Kim, Woojeong
Published: (2026)

One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
by: Auroy, Samuel, et al.
Published: (2025)

Using Parametric PINNs for Predicting Internal and External Turbulent Flows
by: Ghosh, Shinjan, et al.
Published: (2024)

TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
by: Chen, Yanlai, et al.
Published: (2024)

A numerical scheme for a multi-scale model of thrombus in arteries
by: Kim, Woojeong
Published: (2025)

High precision PINNs in unbounded domains: application to singularity formulation in PDEs
by: Wang, Yixuan, et al.
Published: (2025)

Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
by: Bioli, Ivan, et al.
Published: (2025)

Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis
by: Park, Yesom, et al.
Published: (2024)

Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
by: Disarò, Sean, et al.
Published: (2026)

PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
by: Shi, Shengzhu, et al.
Published: (2023)

Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries
by: Rowan, Conor, et al.
Published: (2025)

Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
by: Hillebrecht, Birgit, et al.
Published: (2022)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
by: Liang, Zhangyong
Published: (2026)

PINNs error estimates for nonlinear equations in $\mathbb{R}$-smooth Banach spaces
by: Gao, Jiexing, et al.
Published: (2023)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
by: Gao, Yihang, et al.
Published: (2022)

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

KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers
by: Bounja, Karim, et al.
Published: (2025)

Geometry-aware PINNs for Turbulent Flow Prediction
by: Ghosh, Shinjan, et al.
Published: (2024)

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
by: Zhuang, Bozhou, et al.
Published: (2024)

Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
by: Ren, Mohan, et al.
Published: (2024)

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
by: Liu, Jerry, et al.
Published: (2025)

Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
by: Chaudhry, Faris
Published: (2026)

Bi-fidelity Variational Auto-encoder for Uncertainty Quantification
by: Cheng, Nuojin, et al.
Published: (2023)

DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
by: Hao, Zhongkai, et al.
Published: (2024)

Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
by: Shahab, Muhammad Luthfi, et al.
Published: (2026)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
by: Zheng, Haoyang, et al.
Published: (2023)

Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements
by: Krämer, Nicholas
Published: (2024)

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models
by: Saporiti, Riccardo, et al.
Published: (2026)

Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs
by: Corral, Álvaro Fernández, et al.
Published: (2024)

Matrix Decomposition and Applications
by: Lu, Jun
Published: (2022)

Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
by: Gilbert, Anna C., et al.
Published: (2024)

Multilevel CNNs for Parametric PDEs based on Adaptive Finite Elements
by: Schütte, Janina Enrica, et al.
Published: (2024)

Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model
by: Chen, Yanlai, et al.
Published: (2025)