Cover Image

GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chatzopoulos, Matthaios, Koutsourelakis, Phaedon-Stelios
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media
by: Chatzopoulos, Matthaios, 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)

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
by: Agrawal, Atul, et al.
Published: (2023)

Design-GenNO: A Physics-Informed Generative Model with Neural Operators for Inverse Microstructure Design
by: Zang, Yaohua, et al.
Published: (2025)

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography
by: Scholz, Vincent C., et al.
Published: (2024)

Latent Neural Operator for Solving Forward and Inverse PDE Problems
by: Wang, Tian, et al.
Published: (2024)

PSP-GEN: Stochastic inversion of the Process-Structure-Property chain in materials design through deep, generative probabilistic modeling
by: Zang, Yaohua, et al.
Published: (2024)

The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems
by: Scholz, Vincent C., et al.
Published: (2025)

Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow
by: Nitzler, Jonas, et al.
Published: (2026)

CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems
by: Jacobsen, Christian, et al.
Published: (2023)

Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
by: Liu, Xu, et al.
Published: (2022)

A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations
by: Gupta, Naveen, et al.
Published: (2024)

Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
by: Chen, Yongsheng, et al.
Published: (2026)

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
by: Wu, Tailin, et al.
Published: (2024)

Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data
by: Stupp, Maximilian, et al.
Published: (2025)

Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems
by: Lahoud, Alan A., et al.
Published: (2025)

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory
by: Zhang, Xifeng, et al.
Published: (2026)

Graph Neural Regularizers for PDE Inverse Problems
by: Lauga, William, et al.
Published: (2025)

A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
by: Wang, Housen, et al.
Published: (2024)

Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration
by: Arcones, Daniel Andrés, et al.
Published: (2024)

Efficient Bayesian multi-fidelity inverse analysis for expensive and non-differentiable physics-based simulations in high stochastic dimensions
by: Nitzler, Jonas, et al.
Published: (2025)

Model bias identification for Bayesian calibration of stochastic digital twins of bridges
by: Arcones, Daniel Andrés, et al.
Published: (2023)

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems
by: Long, Da, et al.
Published: (2024)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
by: Mora, Carlos, et al.
Published: (2024)

Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passes
by: Dou, Hongkun, et al.
Published: (2026)

Kernel-Adaptive PI-ELMs for Forward and Inverse Problems in PDEs with Sharp Gradients
by: Dwivedi, Vikas, et al.
Published: (2025)

Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm
by: Papachristodoulou, Andreas, et al.
Published: (2023)

TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems
by: Nguyen, Hai V., et al.
Published: (2024)

Latent Generative Models with Tunable Complexity for Compressed Sensing and other Inverse Problems
by: Gunn, Sean, et al.
Published: (2026)

Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces
by: Chen, Qiuyi, et al.
Published: (2024)

Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulations
by: Chen, Xingzhuo, et al.
Published: (2026)

Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
by: Dwivedi, Vikas, et al.
Published: (2025)

Generative Latent Neural PDE Solver using Flow Matching
by: Li, Zijie, et al.
Published: (2025)

Inverse Evolution Data Augmentation for Neural PDE Solvers
by: Liu, Chaoyu, et al.
Published: (2025)

Moonwalk: Inverse-Forward Differentiation
by: Krylov, Dmitrii, et al.
Published: (2024)

Bayesian Inference for PDE-based Inverse Problems using the Optimization of a Discrete Loss
by: Amoudruz, Lucas, et al.
Published: (2025)

A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
by: Wang, Tim Y. J., et al.
Published: (2025)

Feature-Modulated UFNO for Improved Prediction of Multiphase Flow in Porous Media
by: Abdellatif, Alhasan, et al.
Published: (2025)

Feed-Forward Latent Domain Adaptation
by: Bohdal, Ondrej, et al.
Published: (2022)

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)