{"total":19,"rows":[{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2402.11397","title":"Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications","created":"2024-02-17","cited_by_count":11,"area":null},{"id":"2410.23467","title":"Koopman-informed recurrent neural networks","created":"2024-10-30","cited_by_count":8,"area":null},{"id":"2501.18283","title":"Random Feature Representation Boosting","created":"2025-01-30","cited_by_count":4,"area":null},{"id":"2403.08627","title":"Multifidelity linear regression for scientific machine learning from scarce data","created":"2024-03-13","cited_by_count":3,"area":null},{"id":"2411.06848","title":"Generative Feature Training of Thin 2-Layer Networks","created":"2024-11-11","cited_by_count":2,"area":null},{"id":"2411.17511","title":"Training Hamiltonian neural networks without backpropagation","created":"2024-11-26","cited_by_count":2,"area":null},{"id":"2506.06558","title":"Rapid training of Hamiltonian graph networks using random features","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2402.02593","title":"Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy Environments","created":"2024-02-04","cited_by_count":1,"area":null},{"id":"2507.06038","title":"Fredholm Neural Networks for inverse problems in elliptic PDEs","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2511.00886","title":"HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs","created":"2025-11-02","cited_by_count":1,"area":null},{"id":"2603.00579","title":"DeepAFL: Deep Analytic Federated Learning","created":"2026-02-28","cited_by_count":1,"area":null},{"id":"2510.01012","title":"Random Feature Spiking Neural Networks","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2511.02244","title":"Neural network initialization with nonlinear characteristics and information on hierarchical features","created":"2025-11-04","cited_by_count":0,"area":null},{"id":"2601.14794","title":"RANDSMAPs: Random-Feature/multi-Scale Neural Decoders with Mass Preservation","created":"2026-01-21","cited_by_count":0,"area":null},{"id":"2603.21568","title":"Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach","created":"2026-03-23","cited_by_count":0,"area":null},{"id":"2604.09361","title":"Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2605.25057","title":"Random Neural Network Expressivity for Non-Linear Partial Differential Equations","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2607.28977","title":"Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}