Saved in:
| Main Authors: | Bao, Gang, Zang, Yaohua |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03241 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
by: Scholz, Vincent C., et al.
Published: (2024)
by: Scholz, Vincent C., et al.
Published: (2024)
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025)
by: Zang, Yaohua, et al.
Published: (2025)
An effective physics-informed neural operator framework for predicting wavefields
by: Ma, Xiao, et al.
Published: (2025)
by: Ma, Xiao, et al.
Published: (2025)
A variational inference framework for inverse problems
by: Maestrini, Luca, et al.
Published: (2021)
by: Maestrini, Luca, et al.
Published: (2021)
A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations
by: Zhou, Bei, et al.
Published: (2025)
by: Zhou, Bei, et al.
Published: (2025)
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems
by: Boya, Sumanth Kumar, et al.
Published: (2024)
by: Boya, Sumanth Kumar, et al.
Published: (2024)
An invertible generative model for forward and inverse problems
by: van Leeuwen, Tristan, et al.
Published: (2025)
by: van Leeuwen, Tristan, et al.
Published: (2025)
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
by: Wang, Yizheng, et al.
Published: (2024)
by: Wang, Yizheng, et al.
Published: (2024)
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
by: Cao, Lianghao, et al.
Published: (2024)
by: Cao, Lianghao, et al.
Published: (2024)
Harnessing physics-informed operators for high-dimensional reliability analysis problems
by: Navaneeth, N, et al.
Published: (2024)
by: Navaneeth, N, et al.
Published: (2024)
Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
by: Zhou, Shaoqian, et al.
Published: (2025)
by: Zhou, Shaoqian, et al.
Published: (2025)
Tackling multiphysics problems via finite element-guided physics-informed operator learning
by: Yamazaki, Yusuke, et al.
Published: (2026)
by: Yamazaki, Yusuke, et al.
Published: (2026)
A unifying Bayesian framework for adversarial robustness
by: Arce, Pablo G., et al.
Published: (2025)
by: Arce, Pablo G., et al.
Published: (2025)
Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
by: Hu, Qifeng, et al.
Published: (2025)
by: Hu, Qifeng, et al.
Published: (2025)
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
by: Yamazaki, Yusuke, et al.
Published: (2024)
by: Yamazaki, Yusuke, et al.
Published: (2024)
A unified weighting framework for evaluating nearest neighbour classification
by: Lenz, Oliver Urs, et al.
Published: (2023)
by: Lenz, Oliver Urs, et al.
Published: (2023)
Physics-informed waveform inversion using pretrained wavefield neural operators
by: Huang, Xinquan, et al.
Published: (2025)
by: Huang, Xinquan, et al.
Published: (2025)
How well do generative models solve inverse problems? A benchmark study
by: Krüger, Patrick, et al.
Published: (2026)
by: Krüger, Patrick, et al.
Published: (2026)
KD$^{2}$M: A unifying framework for feature knowledge distillation
by: Montesuma, Eduardo Fernandes
Published: (2025)
by: Montesuma, Eduardo Fernandes
Published: (2025)
A unified framework for hard and soft clustering with regularized optimal transport
by: Diebold, Jean-Frédéric, et al.
Published: (2017)
by: Diebold, Jean-Frédéric, et al.
Published: (2017)
JetPrism: diagnosing convergence for generative simulation and inverse problems in nuclear physics
by: Xia, Zeyu, et al.
Published: (2026)
by: Xia, Zeyu, et al.
Published: (2026)
HFI: A unified framework for training-free detection and implicit watermarking of latent diffusion model generated images
by: Choi, Sungik, et al.
Published: (2024)
by: Choi, Sungik, et al.
Published: (2024)
MEIDNet: Multimodal generative AI framework for inverse materials design
by: Babu, Anand, et al.
Published: (2026)
by: Babu, Anand, et al.
Published: (2026)
Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)
Adaptive operator learning for infinite-dimensional Bayesian inverse problems
by: Gao, Zhiwei, et al.
Published: (2023)
by: Gao, Zhiwei, et al.
Published: (2023)
Avoiding strict saddle points of nonconvex regularized problems
by: Bai, Luwei, et al.
Published: (2024)
by: Bai, Luwei, et al.
Published: (2024)
A unified Bayesian framework for interval hypothesis testing in clinical trials
by: Chakraborty, Abhisek, et al.
Published: (2024)
by: Chakraborty, Abhisek, et al.
Published: (2024)
Towards a unified framework for guided diffusion models
by: Jiao, Yuchen, et al.
Published: (2025)
by: Jiao, Yuchen, et al.
Published: (2025)
SG-DeepONet: Source-generalized deep operator learning for full waveform inversion
by: Guo, Zekai, et al.
Published: (2024)
by: Guo, Zekai, et al.
Published: (2024)
A unifying framework for generalised Bayesian online learning in non-stationary environments
by: Duran-Martin, Gerardo, et al.
Published: (2024)
by: Duran-Martin, Gerardo, et al.
Published: (2024)
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
by: Chowdhury, Prithwijit, et al.
Published: (2026)
by: Chowdhury, Prithwijit, et al.
Published: (2026)
Integrated utilization of equations and small dataset in the Koopman operator: applications to forward and inverse problems
by: Ohta, Ichiro, et al.
Published: (2025)
by: Ohta, Ichiro, et al.
Published: (2025)
Learning truly monotone operators with applications to nonlinear inverse problems
by: Belkouchi, Younes, et al.
Published: (2024)
by: Belkouchi, Younes, et al.
Published: (2024)
ADCNet: a unified framework for predicting the activity of antibody-drug conjugates
by: Chen, Liye, et al.
Published: (2024)
by: Chen, Liye, et al.
Published: (2024)
Towards a unified and verified understanding of group-operation networks
by: Wu, Wilson, et al.
Published: (2024)
by: Wu, Wilson, et al.
Published: (2024)
A unified framework for establishing the universal approximation of transformer-type architectures
by: Cheng, Jingpu, et al.
Published: (2025)
by: Cheng, Jingpu, et al.
Published: (2025)
Rethinking industrial artificial intelligence: a unified foundation framework
by: Lee, Jay, et al.
Published: (2025)
by: Lee, Jay, et al.
Published: (2025)
Event-driven physics-informed operator learning for reliability analysis
by: Garg, Shailesh, et al.
Published: (2025)
by: Garg, Shailesh, et al.
Published: (2025)
An operator preconditioning perspective on training in physics-informed machine learning
by: De Ryck, Tim, et al.
Published: (2023)
by: De Ryck, Tim, et al.
Published: (2023)
How many measurements are enough? Bayesian recovery in inverse problems with general distributions
by: Adcock, Ben, et al.
Published: (2025)
by: Adcock, Ben, et al.
Published: (2025)
Similar Items
-
Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
by: Scholz, Vincent C., et al.
Published: (2024) -
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025) -
An effective physics-informed neural operator framework for predicting wavefields
by: Ma, Xiao, et al.
Published: (2025) -
A variational inference framework for inverse problems
by: Maestrini, Luca, et al.
Published: (2021) -
A unified framework for geometry-independent operator learning in cardiac electrophysiology simulations
by: Zhou, Bei, et al.
Published: (2025)