CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Dong, Zhikang, Polak, Pawel |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs
by: Corral, Álvaro Fernández, et al.
Published: (2024)
by: Corral, Álvaro Fernández, et al.
Published: (2024)
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)
by: Hu, Zheyuan, et al.
Published: (2023)
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations
by: Hu, Zheyuan, et al.
Published: (2024)
by: Hu, Zheyuan, et al.
Published: (2024)
SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
by: Miñoza, Jose Marie Antonio
Published: (2026)
by: Miñoza, Jose Marie Antonio
Published: (2026)
Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
by: Li, Guojie, et al.
Published: (2026)
by: Li, Guojie, et al.
Published: (2026)
Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks
by: Panahi, Milad, et al.
Published: (2024)
by: Panahi, Milad, et al.
Published: (2024)
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
by: Kong, Chun-Wei, et al.
Published: (2024)
by: Kong, Chun-Wei, et al.
Published: (2024)
Weak Collocation Regression method: fast reveal hidden stochastic dynamics from high-dimensional aggregate data
by: Lu, Liwei, et al.
Published: (2022)
by: Lu, Liwei, et al.
Published: (2022)
Weak Collocation Regression for Inferring Stochastic Dynamics with Lévy Noise
by: Guo, Liya, et al.
Published: (2024)
by: Guo, Liya, et al.
Published: (2024)
When is a System Discoverable from Data? Discovery Requires Chaos
by: Shumaylov, Zakhar, et al.
Published: (2025)
by: Shumaylov, Zakhar, et al.
Published: (2025)
Validated integration of semilinear parabolic PDEs
by: Berg, Jan Bouwe van den, et al.
Published: (2023)
by: Berg, Jan Bouwe van den, et al.
Published: (2023)
Sequential data assimilation for PDEs using shape-morphing solutions
by: Hilliard, Zachary T., et al.
Published: (2024)
by: Hilliard, Zachary T., et al.
Published: (2024)
Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
by: Ren, Mohan, et al.
Published: (2024)
by: Ren, Mohan, et al.
Published: (2024)
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)
by: Hu, Zheyuan, et al.
Published: (2023)
A simple rigorous integrator for semilinear parabolic PDEs
by: Berg, Jan Bouwe van den, et al.
Published: (2026)
by: Berg, Jan Bouwe van den, et al.
Published: (2026)
Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEs
by: Fabiani, Gianluca, et al.
Published: (2022)
by: Fabiani, Gianluca, et al.
Published: (2022)
Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2024)
by: Hu, Zheyuan, et al.
Published: (2024)
Improving physics-informed neural network extrapolation via transfer learning and adaptive activation functions
by: Papastathopoulos-Katsaros, Athanasios, et al.
Published: (2025)
by: Papastathopoulos-Katsaros, Athanasios, et al.
Published: (2025)
Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks
by: Alvarez, Hector Vargas, et al.
Published: (2024)
by: Alvarez, Hector Vargas, et al.
Published: (2024)
Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics
by: Hoekstra, Rik, et al.
Published: (2024)
by: Hoekstra, Rik, et al.
Published: (2024)
Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators
by: Zhao, Xuanle, et al.
Published: (2023)
by: Zhao, Xuanle, et al.
Published: (2023)
Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
by: Nagy-Huber, Monika, et al.
Published: (2023)
by: Nagy-Huber, Monika, et al.
Published: (2023)
State Estimation Using Sparse DEIM and Recurrent Neural Networks
by: Farazmand, Mohammad
Published: (2024)
by: Farazmand, Mohammad
Published: (2024)
Direct Finite-Time Contraction (Step-Log) Profiling--Driven Optimization of Parallel Schemes for Nonlinear Problems on Multicore Architectures
by: Shams, Mudassir, et al.
Published: (2026)
by: Shams, Mudassir, et al.
Published: (2026)
Stochastic multisymplectic PDEs and their structure-preserving numerical methods
by: Hu, Ruiao, et al.
Published: (2025)
by: Hu, Ruiao, et al.
Published: (2025)
Fractional Dissipative PDEs
by: Achleitner, Franz, et al.
Published: (2023)
by: Achleitner, Franz, et al.
Published: (2023)
Multi-Condition Digital Twin Calibration for Axial Piston Pumps : Compound Fault Simulation
by: Dong, Chang, et al.
Published: (2026)
by: Dong, Chang, et al.
Published: (2026)
Discontinuity Computing using Physics-Informed Neural Network
by: Liu, Li, et al.
Published: (2022)
by: Liu, Li, et al.
Published: (2022)
Generalized Lagrangian Neural Networks
by: Xiao, Shanshan, et al.
Published: (2024)
by: Xiao, Shanshan, et al.
Published: (2024)
A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network
by: Zhang, Han, et al.
Published: (2023)
by: Zhang, Han, et al.
Published: (2023)
Feedback Integrators: Non-Asymptotic Invariance for One-Step Methods and Gain Selection under Euler Discretization
by: Bae, Juho, et al.
Published: (2025)
by: Bae, Juho, et al.
Published: (2025)
Multiscale Graph Neural Network for Turbulent Flow-Thermal Prediction Around a Complex-Shaped Pin-Fin
by: Raut, Riddhiman, et al.
Published: (2025)
by: Raut, Riddhiman, et al.
Published: (2025)
A Jacobi Field Approach to Splitting Detection in Schrödinger Bridge
by: Jiao, Chunhai, et al.
Published: (2026)
by: Jiao, Chunhai, et al.
Published: (2026)
Randomized time stepping of nonlinearly parametrized solutions of evolution problems
by: Dong, Yijun, et al.
Published: (2025)
by: Dong, Yijun, et al.
Published: (2025)
Using nodal coordinates as variables for the dimensional synthesis of mechanisms
by: Garcia-Marina, V., et al.
Published: (2024)
by: Garcia-Marina, V., et al.
Published: (2024)
Data-driven balanced truncation for linear systems with quadratic outputs
by: Padhi, Reetish, et al.
Published: (2025)
by: Padhi, Reetish, et al.
Published: (2025)
Data-driven Discovery of Delay Differential Equations with Discrete Delays
by: Pecile, Alessandro, et al.
Published: (2024)
by: Pecile, Alessandro, et al.
Published: (2024)
FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model
by: Song, Zezheng, et al.
Published: (2024)
by: Song, Zezheng, et al.
Published: (2024)
Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds
by: Llamazares-Elias, Liam, et al.
Published: (2024)
by: Llamazares-Elias, Liam, et al.
Published: (2024)
AW-EL-PINNs: A Multi-Task Learning Physics-Informed Neural Network for Euler-Lagrange Systems in Optimal Control Problems
by: Li, Chuandong, et al.
Published: (2025)
by: Li, Chuandong, et al.
Published: (2025)
Similar Items
-
Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs
by: Corral, Álvaro Fernández, et al.
Published: (2024) -
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023) -
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations
by: Hu, Zheyuan, et al.
Published: (2024) -
SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
by: Miñoza, Jose Marie Antonio
Published: (2026) -
Incorporating Continuous Dependence Qualifies Physics-Informed Neural Networks for Operator Learning
by: Li, Guojie, et al.
Published: (2026)