{"total":21,"rows":[{"id":"2309.15325","title":"Neural Operators for Accelerating Scientific Simulations and Design","created":"2023-09-27","cited_by_count":221,"area":null},{"id":"2402.11722","title":"Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems","created":"2024-02-18","cited_by_count":19,"area":null},{"id":"2501.08428","title":"Physics-Informed Latent Neural Operator for Real-time Predictions of time-dependent parametric PDEs","created":"2025-01-14","cited_by_count":9,"area":null},{"id":"2301.11040","title":"Random Grid Neural Processes for Parametric Partial Differential Equations","created":"2023-01-26","cited_by_count":8,"area":null},{"id":"2404.17789","title":"BiLO: Bilevel Local Operator Learning for PDE Inverse Problems","created":"2024-04-27","cited_by_count":8,"area":null},{"id":"2412.05133","title":"Learning Hidden Physics and System Parameters with Deep Operator Networks","created":"2024-12-06","cited_by_count":7,"area":null},{"id":"2304.12541","title":"Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems","created":"2023-04-25","cited_by_count":6,"area":null},{"id":"2402.17157","title":"Generative Learning for Forecasting the Dynamics of Complex Systems","created":"2024-02-27","cited_by_count":6,"area":null},{"id":"2305.20004","title":"Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides","created":"2023-05-31","cited_by_count":5,"area":null},{"id":"2407.20697","title":"Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography","created":"2024-07-30","cited_by_count":5,"area":null},{"id":"2502.06250","title":"DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling","created":"2025-02-10","cited_by_count":5,"area":null},{"id":"2402.00825","title":"Resolution invariant deep operator network for PDEs with complex geometries","created":"2024-02-01","cited_by_count":4,"area":null},{"id":"2403.19470","title":"Physics-aware deep learning framework for the limited aperture inverse obstacle scattering problem","created":"2024-03-28","cited_by_count":1,"area":null},{"id":"2509.19929","title":"Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2603.11854","title":"Inverse Neural Operator for ODE Parameter Optimization","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2607.01128","title":"GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems","created":"2026-07-01","cited_by_count":1,"area":null},{"id":"2510.26704","title":"How Regularization Terms Make Invertible Neural Networks Bayesian Point Estimators","created":"2025-10-30","cited_by_count":0,"area":null},{"id":"2512.18120","title":"Learning Generalizable Neural Operators for Inverse Problems","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2512.20268","title":"DeepONet-accelerated Bayesian inversion for moving boundary problems","created":"2025-12-23","cited_by_count":0,"area":null},{"id":"2602.14642","title":"GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media","created":"2026-02-16","cited_by_count":0,"area":null},{"id":"2605.03247","title":"Posterior-First Neural PDE Simulation: Inferring Hidden Problem State from a Single Field","created":"2026-05-05","cited_by_count":0,"area":null}],"next_cursor":null}