AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
Fuente:
arXiv
Saved in:
| Main Authors: | Botvinick-Greenhouse, Jonah, Ali, Wael H., Benosman, Mouhacine, Mowlavi, Saviz |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Reinforcement learning-based estimation for partial differential equations
by: Mowlavi, Saviz, et al.
Published: (2023)
by: Mowlavi, Saviz, et al.
Published: (2023)
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
by: Xie, Xiaoyu, et al.
Published: (2024)
by: Xie, Xiaoyu, et al.
Published: (2024)
Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application
by: Botvinick-Greenhouse, Jonah
Published: (2025)
by: Botvinick-Greenhouse, Jonah
Published: (2025)
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
by: Zhang, Xiangyuan, et al.
Published: (2023)
by: Zhang, Xiangyuan, et al.
Published: (2023)
A Dual Ensemble Kalman Filter Approach to Robust Control of Nonlinear Systems: An Application to Partial Differential Equations
by: Joshi, Anant A., et al.
Published: (2025)
by: Joshi, Anant A., et al.
Published: (2025)
On the Unique Recovery of Transport Maps and Vector Fields from Finite Measure-Valued Data
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2026)
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2026)
Policy Optimization for PDE Control with a Warm Start
by: Zhang, Xiangyuan, et al.
Published: (2024)
by: Zhang, Xiangyuan, et al.
Published: (2024)
Physics-Informed Deep B-Spline Networks
by: Wang, Zhuoyuan, et al.
Published: (2025)
by: Wang, Zhuoyuan, et al.
Published: (2025)
Invariant Measures in Time-Delay Coordinates for Unique Dynamical System Identification
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)
Measure-Theoretic Time-Delay Embedding
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)
by: Botvinick-Greenhouse, Jonah, et al.
Published: (2024)
Detecting hidden structures from a static loading experiment: topology optimization meets physics-informed neural networks
by: Mowlavi, Saviz, et al.
Published: (2023)
by: Mowlavi, Saviz, et al.
Published: (2023)
Physics-Informed Neural Controlled Differential Equations for Scalable Long Horizon Multi-Agent Motion Forecasting
by: Sural, Shounak, et al.
Published: (2025)
by: Sural, Shounak, et al.
Published: (2025)
RRaPINNs: Residual Risk-Aware Physics Informed Neural Networks
by: Akazan, Ange-Clément, et al.
Published: (2025)
by: Akazan, Ange-Clément, et al.
Published: (2025)
On The Convergence of Euler Discretization of Finite-Time Convergent Gradient Flows
by: Zhang, Siqi, et al.
Published: (2020)
by: Zhang, Siqi, et al.
Published: (2020)
Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts
by: Rodriguez, Arturo, et al.
Published: (2024)
by: Rodriguez, Arturo, et al.
Published: (2024)
Designing Psychometric Bias Measures for ChatBots: An Application to Racial Bias Measurement
by: Benosman, Mouhacine
Published: (2025)
by: Benosman, Mouhacine
Published: (2025)
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
by: Gao, Yihang, et al.
Published: (2022)
by: Gao, Yihang, et al.
Published: (2022)
E-PINNs: Epistemic Physics-Informed Neural Networks
by: Jacob, Bruno, et al.
Published: (2025)
by: Jacob, Bruno, et al.
Published: (2025)
HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability
by: Cooley, Madison, et al.
Published: (2024)
by: Cooley, Madison, et al.
Published: (2024)
OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
by: Wang, Zhuoyuan, et al.
Published: (2026)
by: Wang, Zhuoyuan, et al.
Published: (2026)
OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering
by: Schperberg, Alexander, et al.
Published: (2024)
by: Schperberg, Alexander, et al.
Published: (2024)
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)
by: Si, Chenhao, et al.
Published: (2024)
by: Si, Chenhao, et al.
Published: (2024)
Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
by: Roy, Sumanta, et al.
Published: (2024)
by: Roy, Sumanta, et al.
Published: (2024)
PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
by: Bonfanti, Andrea, et al.
Published: (2025)
by: Bonfanti, Andrea, et al.
Published: (2025)
Multi-robot Path Planning and Scheduling via Model Predictive Optimal Transport (MPC-OT)
by: Khan, Usman A., et al.
Published: (2025)
by: Khan, Usman A., et al.
Published: (2025)
Feature Mapping in Physics-Informed Neural Networks (PINNs)
by: Zeng, Chengxi, et al.
Published: (2024)
by: Zeng, Chengxi, et al.
Published: (2024)
Reduced‐Order, Data‐Driven, and Decomposition Methods for Modelling, Identification, and Estimation
by: Angelo Alessandri, et al.
Published: (2025)
by: Angelo Alessandri, et al.
Published: (2025)
Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks
by: Jiang, Feilong, et al.
Published: (2025)
by: Jiang, Feilong, et al.
Published: (2025)
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
by: Nagesh, Chandra Kanth, et al.
Published: (2025)
by: Nagesh, Chandra Kanth, et al.
Published: (2025)
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs)
by: Abbasi, Jassem, et al.
Published: (2022)
by: Abbasi, Jassem, et al.
Published: (2022)
NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations
by: Makki, Maedeh, et al.
Published: (2026)
by: Makki, Maedeh, et al.
Published: (2026)
GRAM: Generalization in Deep RL with a Robust Adaptation Module
by: Queeney, James, et al.
Published: (2024)
by: Queeney, James, et al.
Published: (2024)
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
by: Zhao, Zhiyuan, et al.
Published: (2023)
by: Zhao, Zhiyuan, et al.
Published: (2023)
Functional Tensor Decompositions for Physics-Informed Neural Networks
by: Vemuri, Sai Karthikeya, et al.
Published: (2024)
by: Vemuri, Sai Karthikeya, et al.
Published: (2024)
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
by: Song, Zhenao
Published: (2025)
by: Song, Zhenao
Published: (2025)
$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
by: Figueres, Júlia Vicens, et al.
Published: (2025)
by: Figueres, Júlia Vicens, et al.
Published: (2025)
SetPINNs: Set-based Physics-informed Neural Networks
by: Nagda, Mayank, et al.
Published: (2024)
by: Nagda, Mayank, et al.
Published: (2024)
CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks
by: Dong, Zhikang, et al.
Published: (2022)
by: Dong, Zhikang, et al.
Published: (2022)
Data-Driven Variational Basis Learning Beyond Neural Networks: A Non-Neural Framework for Adaptive Basis Discovery
by: Kiruluta, Andrew
Published: (2026)
by: Kiruluta, Andrew
Published: (2026)
Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs
by: Huo, Wenxuan, et al.
Published: (2025)
by: Huo, Wenxuan, et al.
Published: (2025)
Similar Items
-
Reinforcement learning-based estimation for partial differential equations
by: Mowlavi, Saviz, et al.
Published: (2023) -
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
by: Xie, Xiaoyu, et al.
Published: (2024) -
Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application
by: Botvinick-Greenhouse, Jonah
Published: (2025) -
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms
by: Zhang, Xiangyuan, et al.
Published: (2023) -
A Dual Ensemble Kalman Filter Approach to Robust Control of Nonlinear Systems: An Application to Partial Differential Equations
by: Joshi, Anant A., et al.
Published: (2025)