Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
Fuente:
arXiv
Saved in:
| Main Authors: | Mohammad-Djafari, Ali, Chu, Ning, Wang, Li |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bayesian PINNs for uncertainty-aware inverse problems (BPINN-IP)
by: Mohammad-Djafari, Ali
Published: (2026)
by: Mohammad-Djafari, Ali
Published: (2026)
Bayesian Physics Informed Neural Networks for Linear Inverse problems
by: Mohammad-Djafari, Ali
Published: (2025)
by: Mohammad-Djafari, Ali
Published: (2025)
Model Based and Physics Informed Deep Learning Neural Network Structures
by: Mohammad-Djafari, Ali, et al.
Published: (2024)
by: Mohammad-Djafari, Ali, et al.
Published: (2024)
Physics-Informed Neural Networks with Unknown Partial Differential Equations: an Application in Multivariate Time Series
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2025)
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2025)
BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase
by: Lamichhane, Subed, et al.
Published: (2025)
by: Lamichhane, Subed, et al.
Published: (2025)
GraIP: A Benchmarking Framework For Neural Graph Inverse Problems
by: Cantürk, Semih, et al.
Published: (2026)
by: Cantürk, Semih, et al.
Published: (2026)
Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
by: Jekic, Aleksandra, et al.
Published: (2025)
by: Jekic, Aleksandra, et al.
Published: (2025)
Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)
by: Sahin, T., et al.
Published: (2023)
Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)
by: Du, Yutong, et al.
Published: (2025)
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference
by: Niven, Robert K., et al.
Published: (2024)
by: Niven, Robert K., et al.
Published: (2024)
Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)
by: Zhou, Wei, et al.
Published: (2024)
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
by: Liu, Xu, et al.
Published: (2022)
by: Liu, Xu, et al.
Published: (2022)
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
by: Hou, Yilong, et al.
Published: (2024)
by: Hou, Yilong, et al.
Published: (2024)
IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation
by: Manor, Shalev, et al.
Published: (2025)
by: Manor, Shalev, et al.
Published: (2025)
Conformalized Physics-Informed Neural Networks
by: Podina, Lena, et al.
Published: (2024)
by: Podina, Lena, et al.
Published: (2024)
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks
by: Makrygiorgos, Georgios, et al.
Published: (2025)
by: Makrygiorgos, Georgios, et al.
Published: (2025)
eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
by: Lotfalian, Amin, et al.
Published: (2025)
by: Lotfalian, Amin, et al.
Published: (2025)
Majorization-Minimization Networks for Inverse Problems: An Application to EEG Imaging
by: Tran, Le Minh Triet, et al.
Published: (2026)
by: Tran, Le Minh Triet, et al.
Published: (2026)
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation
by: Zong, Yifei, et al.
Published: (2024)
by: Zong, Yifei, et al.
Published: (2024)
ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems
by: Orozco, Rafael, et al.
Published: (2024)
by: Orozco, Rafael, et al.
Published: (2024)
Bayesian Reasoning for Physics Informed Neural Networks
by: Graczyk, Krzysztof M., et al.
Published: (2023)
by: Graczyk, Krzysztof M., et al.
Published: (2023)
Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks
by: Podina, Lena, et al.
Published: (2024)
by: Podina, Lena, et al.
Published: (2024)
Bayesian Model Parameter Learning in Linear Inverse Problems: Application in EEG Focal Source Imaging
by: Koulouri, Alexandra, et al.
Published: (2025)
by: Koulouri, Alexandra, et al.
Published: (2025)
Physics-Informed Neural Network for Concrete Manufacturing Process Optimization
by: Varghese, Sam, et al.
Published: (2024)
by: Varghese, Sam, et al.
Published: (2024)
The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks
by: Holl, Philipp, et al.
Published: (2024)
by: Holl, Philipp, et al.
Published: (2024)
Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
by: Ramirez, Ibai, et al.
Published: (2025)
by: Ramirez, Ibai, et al.
Published: (2025)
Spatiotemporal Besov Priors for Bayesian Inverse Problems
by: Lan, Shiwei, et al.
Published: (2023)
by: Lan, Shiwei, et al.
Published: (2023)
Structure-Preserving Physics-Informed Neural Networks With Energy or Lyapunov Structure
by: Chu, Haoyu, et al.
Published: (2024)
by: Chu, Haoyu, et al.
Published: (2024)
Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)
by: Du, Yutong, et al.
Published: (2025)
Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2026)
by: Du, Yutong, et al.
Published: (2026)
Variational Graph Neural Networks for Uncertainty Quantification in Inverse Problems
by: Gonzalez, David, et al.
Published: (2026)
by: Gonzalez, David, et al.
Published: (2026)
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
by: Buskulic, Nathan, et al.
Published: (2023)
by: Buskulic, Nathan, et al.
Published: (2023)
Advancing Solutions for the Three-Body Problem Through Physics-Informed Neural Networks
by: Pereira, Manuel Santos, et al.
Published: (2025)
by: Pereira, Manuel Santos, et al.
Published: (2025)
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences
by: Zheng, Hongkai, et al.
Published: (2025)
by: Zheng, Hongkai, et al.
Published: (2025)
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)
by: Velioglu, Mehmet, et al.
Published: (2024)
Applications and Manipulations of Physics-Informed Neural Networks in Solving Differential Equations
by: Gupta, Aarush, et al.
Published: (2025)
by: Gupta, Aarush, et al.
Published: (2025)
Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
by: Wei, Zhao, et al.
Published: (2026)
by: Wei, Zhao, et al.
Published: (2026)
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
by: Zhang, Huiwen, et al.
Published: (2026)
by: Zhang, Huiwen, et al.
Published: (2026)
ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks
by: Liu, Qiang, et al.
Published: (2024)
by: Liu, Qiang, et al.
Published: (2024)
Outlier-robust Diffusion Posterior Sampling for Bayesian Inverse Problems
by: Yang, Yiming, et al.
Published: (2026)
by: Yang, Yiming, et al.
Published: (2026)
Similar Items
-
Bayesian PINNs for uncertainty-aware inverse problems (BPINN-IP)
by: Mohammad-Djafari, Ali
Published: (2026) -
Bayesian Physics Informed Neural Networks for Linear Inverse problems
by: Mohammad-Djafari, Ali
Published: (2025) -
Model Based and Physics Informed Deep Learning Neural Network Structures
by: Mohammad-Djafari, Ali, et al.
Published: (2024) -
Physics-Informed Neural Networks with Unknown Partial Differential Equations: an Application in Multivariate Time Series
by: Mortezanejad, Seyedeh Azadeh Fallah, et al.
Published: (2025) -
BPINN-EM-Post: Bayesian Physics-Informed Neural Network based Stochastic Electromigration Damage Analysis in the Post-void Phase
by: Lamichhane, Subed, et al.
Published: (2025)