{"total":34,"rows":[{"id":"2111.02801","title":"Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems","created":"2021-11-01","cited_by_count":222,"area":null},{"id":"2201.07395","title":"Overview frequency principle/spectral bias in deep learning","created":"2022-01-19","cited_by_count":69,"area":null},{"id":"2105.11686","title":"Towards Understanding the Condensation of Neural Networks at Initial Training","created":"2021-05-25","cited_by_count":32,"area":null},{"id":"2208.04924","title":"On the Activation Function Dependence of the Spectral Bias of Neural Networks","created":"2022-08-09","cited_by_count":28,"area":null},{"id":"2107.03673","title":"MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs","created":"2021-07-08","cited_by_count":24,"area":null},{"id":"2101.04844","title":"Reproducing Activation Function for Deep Learning","created":"2021-01-13","cited_by_count":19,"area":null},{"id":"2410.16105","title":"Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning","created":"2024-10-21","cited_by_count":14,"area":null},{"id":"2206.09299","title":"Enforcing continuous symmetries in physics-informed neural network for solving forward and inverse problems of partial differential equations","created":"2022-06-19","cited_by_count":11,"area":null},{"id":"2112.06660","title":"Subspace Decomposition based DNN algorithm for elliptic type multi-scale PDEs","created":"2021-12-10","cited_by_count":10,"area":null},{"id":"2210.13212","title":"A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency","created":"2022-10-19","cited_by_count":7,"area":null},{"id":"2302.00045","title":"Neural Control of Parametric Solutions for High-dimensional Evolution PDEs","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2309.07401","title":"Multi-Grade Deep Learning for Partial Differential Equations with Applications to the Burgers Equation","created":"2023-09-14","cited_by_count":5,"area":null},{"id":"2410.00053","title":"Frequency-adaptive Multi-scale Deep Neural Networks","created":"2024-09-28","cited_by_count":5,"area":null},{"id":"2506.22555","title":"Spectral Bias in Variational Quantum Machine Learning","created":"2025-06-27","cited_by_count":5,"area":null},{"id":"2105.11675","title":"An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network","created":"2021-05-25","cited_by_count":4,"area":null},{"id":"2502.18959","title":"Fourier Multi-Component and Multi-Layer Neural Networks: Unlocking High-Frequency Potential","created":"2025-02-26","cited_by_count":4,"area":null},{"id":"2202.04137","title":"Machine Learning in Heterogeneous Porous Materials","created":"2022-02-04","cited_by_count":3,"area":null},{"id":"2406.03407","title":"Physics and geometry informed neural operator network with application to acoustic scattering","created":"2024-06-02","cited_by_count":3,"area":null},{"id":"2212.00270","title":"On the Compatibility between Neural Networks and Partial Differential Equations for Physics-informed Learning","created":"2022-12-01","cited_by_count":2,"area":null},{"id":"2509.19331","title":"Holographic Transformers for Complex-Valued Signal Processing: Integrating Phase Interference into Self-Attention","created":"2025-09-14","cited_by_count":2,"area":null},{"id":"2604.20219","title":"Layer-wise Geometric Approximation Rates for Deep Networks","created":"2026-04-22","cited_by_count":2,"area":null},{"id":"2411.11497","title":"Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models","created":"2024-11-18","cited_by_count":1,"area":null},{"id":"2510.20970","title":"On the accuracy of implicit neural representations for cardiovascular anatomies and hemodynamic fields","created":"2025-10-23","cited_by_count":1,"area":null},{"id":"2511.01804","title":"Dynamic Reconstruction of Ultrasound-Derived Flow Fields With Physics-Informed Neural Fields","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2106.10401","title":"Parallel frequency function-deep neural network for efficient complex broadband signal approximation","created":"2021-06-19","cited_by_count":0,"area":null},{"id":"2301.04785","title":"Phase-shifted Adversarial Training","created":"2023-01-12","cited_by_count":0,"area":null},{"id":"2304.14604","title":"Deep Neural-network Prior for Orbit Recovery from Method of Moments","created":"2023-04-28","cited_by_count":0,"area":null},{"id":"2311.00860","title":"Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning","created":"2023-11-01","cited_by_count":0,"area":null},{"id":"2311.03746","title":"Enhanced physics-informed neural networks with domain scaling and residual correction methods for multi-frequency elliptic problems","created":"2023-11-07","cited_by_count":0,"area":null},{"id":"2401.10385","title":"Approximation of Solution Operators for High-dimensional PDEs","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2402.17232","title":"Two-scale Neural Networks for Partial Differential Equations with Small Parameters","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2508.15198","title":"Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2512.12898","title":"Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2610.03378","title":"Operator-informed initialization for Fourier features physics-informed neural networks","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}