{"total":18,"rows":[{"id":"2204.09157","title":"Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems","created":"2022-04-19","cited_by_count":58,"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":"2208.05606","title":"Multi-fidelity wavelet neural operator with application to uncertainty quantification","created":"2022-08-11","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":"2304.06972","title":"Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator","created":"2023-04-14","cited_by_count":10,"area":null},{"id":"2410.14764","title":"Multifidelity Kolmogorov-Arnold Networks","created":"2024-10-18","cited_by_count":9,"area":null},{"id":"2303.11577","title":"Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations","created":"2023-03-21","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":"2308.09113","title":"Multi-fidelity Fourier Neural Operator for Fast Modeling of Large-Scale Geological Carbon Storage","created":"2023-08-17","cited_by_count":5,"area":null},{"id":"2503.19081","title":"Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models","created":"2025-03-24","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":"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":"2107.10661","title":"Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders","created":"2021-07-19","cited_by_count":2,"area":null},{"id":"2512.02868","title":"Assessing the performance of correlation-based multi-fidelity neural emulators","created":"2025-12-02","cited_by_count":2,"area":null},{"id":"2509.13138","title":"Curriculum Learning for Mesh-based simulations","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2306.06904","title":"Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning","created":"2023-06-12","cited_by_count":0,"area":null},{"id":"2511.01830","title":"Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2609.08947","title":"ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows","created":"2026-09-08","cited_by_count":0,"area":null}],"next_cursor":null}