{"total":24,"rows":[{"id":"2209.02772","title":"Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems","created":"2022-09-06","cited_by_count":25,"area":null},{"id":"2105.00856","title":"Transfer Learning on Multi-Fidelity Data","created":"2021-04-29","cited_by_count":18,"area":null},{"id":"2107.09814","title":"Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models","created":"2021-07-21","cited_by_count":16,"area":null},{"id":"2102.03169","title":"Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems","created":"2021-02-04","cited_by_count":12,"area":null},{"id":"2204.11138","title":"Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models","created":"2022-04-23","cited_by_count":11,"area":null},{"id":"2302.07160","title":"Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs","created":"2023-02-14","cited_by_count":6,"area":null},{"id":"2401.16683","title":"Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification","created":"2024-01-30","cited_by_count":6,"area":null},{"id":"2111.00745","title":"Uncertainty quantification for ptychography using normalizing flows","created":"2021-11-01","cited_by_count":4,"area":null},{"id":"2203.07524","title":"Convolutional-Recurrent Neural Network Proxy for Robust Optimization and Closed-Loop Reservoir Management","created":"2022-03-14","cited_by_count":4,"area":null},{"id":"2302.11173","title":"VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems","created":"2023-02-22","cited_by_count":4,"area":null},{"id":"2310.06319","title":"Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls","created":"2023-10-10","cited_by_count":4,"area":null},{"id":"2110.12367","title":"Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties","created":"2021-10-24","cited_by_count":3,"area":null},{"id":"2111.08691","title":"Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network","created":"2021-11-14","cited_by_count":3,"area":null},{"id":"2408.01575","title":"Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Data","created":"2024-08-02","cited_by_count":3,"area":null},{"id":"2410.12241","title":"Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2101.09361","title":"Improving prediction of the terrestrial water storage anomalies during the GRACE and GRACE-FO gap with Bayesian convolutional neural networks","created":"2021-01-21","cited_by_count":2,"area":null},{"id":"2104.04764","title":"Applications of physics-informed scientific machine learning in subsurface science: A survey","created":"2021-04-10","cited_by_count":2,"area":null},{"id":"2301.11279","title":"Gaussian process regression and conditional Karhunen-Lo\\'{e}ve models for data assimilation in inverse problems","created":"2023-01-26","cited_by_count":2,"area":null},{"id":"2410.20118","title":"GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction","created":"2024-10-26","cited_by_count":1,"area":null},{"id":"2205.00134","title":"Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net","created":"2022-04-30","cited_by_count":0,"area":null},{"id":"2207.03909","title":"Ensemble random forest filter: An alternative to the ensemble Kalman filter for inverse modeling","created":"2022-07-08","cited_by_count":0,"area":null},{"id":"2210.11737","title":"Bayesian deep learning framework for uncertainty quantification in high dimensions","created":"2022-10-21","cited_by_count":0,"area":null},{"id":"2309.00802","title":"Deep Learning and Inverse Problems","created":"2023-09-02","cited_by_count":0,"area":null},{"id":"2502.17512","title":"Learning multi-phase flow and transport in fractured porous media with auto-regressive and recurrent graph neural networks","created":"2025-02-22","cited_by_count":0,"area":null}],"next_cursor":null}