{"total":135,"rows":[{"id":"2202.07141","title":"Machine Learning in Aerodynamic Shape Optimization","created":"2022-02-15","cited_by_count":39,"area":null},{"id":"2102.13403","title":"Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities","created":"2021-02-26","cited_by_count":31,"area":null},{"id":"2208.14197","title":"A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies","created":"2022-08-26","cited_by_count":28,"area":null},{"id":"2201.00272","title":"Thinking inside the box: A tutorial on grey-box Bayesian optimization","created":"2022-01-02","cited_by_count":26,"area":null},{"id":"2106.13361","title":"Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)","created":"2021-06-25","cited_by_count":25,"area":null},{"id":"2110.13293","title":"Emulation of physical processes with Emukit","created":"2021-10-25","cited_by_count":24,"area":null},{"id":"2210.03008","title":"Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems","created":"2022-10-06","cited_by_count":21,"area":null},{"id":"2408.06526","title":"Operator Learning Using Random Features: A Tool for Scientific Computing","created":"2024-08-12","cited_by_count":21,"area":null},{"id":"2208.03115","title":"Multi-fidelity surrogate modeling using long short-term memory networks","created":"2022-08-05","cited_by_count":18,"area":null},{"id":"2309.00325","title":"Multi-fidelity reduced-order surrogate modeling","created":"2023-09-01","cited_by_count":18,"area":null},{"id":"2303.01560","title":"Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal","created":"2023-03-02","cited_by_count":17,"area":null},{"id":"2112.02206","title":"Data Fusion with Latent Map Gaussian Processes","created":"2021-12-04","cited_by_count":16,"area":null},{"id":"2206.04872","title":"Multi-fidelity Hierarchical Neural Processes","created":"2022-06-10","cited_by_count":15,"area":null},{"id":"2306.11715","title":"Multi-Fidelity Active Learning with GFlowNets","created":"2023-06-20","cited_by_count":15,"area":null},{"id":"2302.12682","title":"A DeepONet multi-fidelity approach for residual learning in reduced order modeling","created":"2023-02-24","cited_by_count":14,"area":null},{"id":"2402.18846","title":"Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling","created":"2024-02-29","cited_by_count":13,"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":"2212.01418","title":"Operator inference with roll outs for learning reduced models from scarce and low-quality data","created":"2022-12-02","cited_by_count":11,"area":null},{"id":"2303.02063","title":"Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook","created":"2023-03-03","cited_by_count":11,"area":null},{"id":"2305.04392","title":"Disentangled Multi-Fidelity Deep Bayesian Active Learning","created":"2023-05-07","cited_by_count":11,"area":null},{"id":"2306.00945","title":"CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions","created":"2023-06-01","cited_by_count":11,"area":null},{"id":"2105.11357","title":"Entropy-based adaptive design for contour finding and estimating reliability","created":"2021-05-24","cited_by_count":10,"area":null},{"id":"2110.02827","title":"Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing","created":"2021-10-06","cited_by_count":10,"area":null},{"id":"2310.03298","title":"A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling","created":"2023-10-05","cited_by_count":10,"area":null},{"id":"2406.17119","title":"Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone","created":"2024-06-24","cited_by_count":10,"area":null},{"id":"2410.12000","title":"Parametric model reduction of mean-field and stochastic systems via higher-order action matching","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2410.14764","title":"Multifidelity Kolmogorov-Arnold Networks","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2502.08416","title":"Multifidelity Simulation-based Inference for Computationally Expensive Simulators","created":"2025-02-12","cited_by_count":9,"area":null},{"id":"2104.03743","title":"Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations","created":"2021-04-08","cited_by_count":8,"area":null},{"id":"2106.13790","title":"Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation","created":"2021-06-25","cited_by_count":8,"area":null},{"id":"2111.14008","title":"Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling","created":"2021-11-28","cited_by_count":8,"area":null},{"id":"2401.13836","title":"Machine learning for industrial sensing and control: A survey and practical perspective","created":"2024-01-24","cited_by_count":8,"area":null},{"id":"2301.06674","title":"Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network","created":"2023-01-17","cited_by_count":7,"area":null},{"id":"2303.04756","title":"Meta-learning Control Variates: Variance Reduction with Limited Data","created":"2023-03-08","cited_by_count":7,"area":null},{"id":"2111.06173","title":"Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling","created":"2021-11-11","cited_by_count":6,"area":null},{"id":"2402.09638","title":"Multi-Fidelity Methods for Optimization: A Survey","created":"2024-02-15","cited_by_count":6,"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":"2506.06087","title":"Multilevel neural simulation-based inference","created":"2025-06-06","cited_by_count":5,"area":null},{"id":"2509.12224","title":"TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks","created":"2025-09-05","cited_by_count":5,"area":null},{"id":"2102.13025","title":"Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders","created":"2021-02-25","cited_by_count":4,"area":null},{"id":"2104.01945","title":"Multilevel Stein variational gradient descent with applications to Bayesian inverse problems","created":"2021-04-05","cited_by_count":4,"area":null},{"id":"2205.12419","title":"Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling","created":"2022-05-25","cited_by_count":4,"area":null},{"id":"2210.01534","title":"Multi-fidelity Monte Carlo: a pseudo-marginal approach","created":"2022-10-04","cited_by_count":4,"area":null},{"id":"2309.10231","title":"Multi-fidelity climate model parameterization for better generalization and extrapolation","created":"2023-09-19","cited_by_count":4,"area":null},{"id":"2310.20153","title":"Interactive Multi-fidelity Learning for Cost-effective Adaptation of Language Model with Sparse Human Supervision","created":"2023-10-31","cited_by_count":4,"area":null},{"id":"2402.02111","title":"Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need","created":"2024-02-03","cited_by_count":4,"area":null},{"id":"2404.11965","title":"Multi-fidelity Gaussian process surrogate modeling for regression problems in physics","created":"2024-04-18","cited_by_count":4,"area":null},{"id":"2201.07130","title":"Online, Informative MCMC Thinning with Kernelized Stein Discrepancy","created":"2022-01-18","cited_by_count":3,"area":null},{"id":"2202.04359","title":"Cost-effective Framework for Gradual Domain Adaptation with Multifidelity","created":"2022-02-09","cited_by_count":3,"area":null},{"id":"2204.02583","title":"PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations","created":"2022-04-06","cited_by_count":3,"area":null},{"id":"2206.05165","title":"Multifidelity Reinforcement Learning with Control Variates","created":"2022-06-10","cited_by_count":3,"area":null},{"id":"2211.10835","title":"Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification","created":"2022-11-20","cited_by_count":3,"area":null},{"id":"2301.05729","title":"GAR: Generalized Autoregression for Multi-Fidelity Fusion","created":"2023-01-13","cited_by_count":3,"area":null},{"id":"2308.00978","title":"Certified Multi-Fidelity Zeroth-Order Optimization","created":"2023-08-02","cited_by_count":3,"area":null},{"id":"2311.15137","title":"Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators","created":"2023-11-25","cited_by_count":3,"area":null},{"id":"2405.01975","title":"Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space","created":"2024-05-03","cited_by_count":3,"area":null},{"id":"2410.23482","title":"Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization","created":"2024-10-30","cited_by_count":3,"area":null},{"id":"2507.22539","title":"A surrogate model for topology optimisation of elastic structures via parametric autoencoders","created":"2025-07-30","cited_by_count":3,"area":null},{"id":"2103.03391","title":"Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation","created":"2021-03-05","cited_by_count":2,"area":null},{"id":"2103.15342","title":"A bandit-learning approach to multifidelity approximation","created":"2021-03-29","cited_by_count":2,"area":null},{"id":"2207.06325","title":"Non-Myopic Multifidelity Bayesian Optimization","created":"2022-07-13","cited_by_count":2,"area":null},{"id":"2212.04309","title":"Deep Variational Inverse Scattering","created":"2022-12-08","cited_by_count":2,"area":null},{"id":"2301.13749","title":"Multi-Fidelity Covariance Estimation in the Log-Euclidean Geometry","created":"2023-01-31","cited_by_count":2,"area":null},{"id":"2412.11257","title":"Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate","created":"2024-12-15","cited_by_count":2,"area":null},{"id":"2502.00550","title":"Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs","created":"2025-02-01","cited_by_count":2,"area":null},{"id":"2510.13762","title":"Progressive multi-fidelity learning with neural networks for physical system predictions","created":"2025-10-15","cited_by_count":2,"area":null},{"id":"2602.21357","title":"Conditional neural control variates for variance reduction in Bayesian inverse problems","created":"2026-02-24","cited_by_count":2,"area":null},{"id":"2605.11859","title":"EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2106.14655","title":"GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins","created":"2021-06-24","cited_by_count":1,"area":null},{"id":"2109.03366","title":"Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose","created":"2021-09-07","cited_by_count":1,"area":null},{"id":"2201.02172","title":"Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation","created":"2022-01-06","cited_by_count":1,"area":null},{"id":"2208.07767","title":"Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression","created":"2022-08-16","cited_by_count":1,"area":null},{"id":"2211.14493","title":"Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data","created":"2022-11-26","cited_by_count":1,"area":null},{"id":"2212.03375","title":"General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation","created":"2022-12-07","cited_by_count":1,"area":null},{"id":"2302.08329","title":"Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications","created":"2023-02-14","cited_by_count":1,"area":null},{"id":"2307.12438","title":"Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices","created":"2023-07-23","cited_by_count":1,"area":null},{"id":"2309.06628","title":"Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning","created":"2023-09-12","cited_by_count":1,"area":null},{"id":"2401.06447","title":"Uncertainty-aware multi-fidelity surrogate modeling with noisy data","created":"2024-01-12","cited_by_count":1,"area":null},{"id":"2409.07510","title":"Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation","created":"2024-09-11","cited_by_count":1,"area":null},{"id":"2505.03246","title":"Bayesian full waveform inversion with sequential surrogate model refinement","created":"2025-05-06","cited_by_count":1,"area":null},{"id":"2506.11683","title":"On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions","created":"2025-06-13","cited_by_count":1,"area":null},{"id":"2507.17713","title":"Sequential Bayesian Design for Efficient Surrogate Construction in the Inversion of Darcy Flows","created":"2025-07-23","cited_by_count":1,"area":null},{"id":"2508.00734","title":"Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2511.00670","title":"Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins","created":"2025-11-01","cited_by_count":1,"area":null},{"id":"2512.05241","title":"Bridging quantum and classical computing for partial differential equations through multifidelity machine learning","created":"2025-12-04","cited_by_count":1,"area":null},{"id":"2512.12276","title":"Balancing Accuracy and Speed: A Multi-Fidelity Ensemble Kalman Filter with a Machine Learning Surrogate Model","created":"2025-12-13","cited_by_count":1,"area":null},{"id":"2512.16430","title":"Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks","created":"2025-12-18","cited_by_count":1,"area":null},{"id":"2601.20888","title":"Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2603.14143","title":"Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors","created":"2026-03-14","cited_by_count":1,"area":null},{"id":"2605.26285","title":"Two-Parameter Flows for Learning Population Dynamics of Physical Systems","created":"2026-05-25","cited_by_count":1,"area":null},{"id":"2606.15690","title":"Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements","created":"2026-06-14","cited_by_count":1,"area":null},{"id":"2606.26728","title":"Scientific discovery as meta-optimization: a combinatorial optimization case study","created":"2026-06-25","cited_by_count":1,"area":null},{"id":"2608.17248","title":"Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data","created":"2026-08-18","cited_by_count":1,"area":null},{"id":"2103.07776","title":"Problem-fluent models for complex decision-making in autonomous materials research","created":"2021-03-13","cited_by_count":0,"area":null},{"id":"2103.12247","title":"Gradient-enhanced multifidelity neural networks for high-dimensional function approximation","created":"2021-03-23","cited_by_count":0,"area":null},{"id":"2109.01887","title":"Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke","created":"2021-09-04","cited_by_count":0,"area":null},{"id":"2110.12658","title":"Operator Shifting for Model-based Policy Evaluation","created":"2021-10-25","cited_by_count":0,"area":null},{"id":"2201.11404","title":"Online Planning in POMDPs with Self-Improving Simulators","created":"2022-01-27","cited_by_count":0,"area":null},{"id":"2204.04819","title":"RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification","created":"2022-04-11","cited_by_count":0,"area":null},{"id":"2207.12854","title":"Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems","created":"2022-07-07","cited_by_count":0,"area":null}],"next_cursor":"100"}