{"total":13,"rows":[{"id":"2408.15183","title":"On latent dynamics learning in nonlinear reduced order modeling","created":"2024-08-27","cited_by_count":14,"area":null},{"id":"2212.04851","title":"A perspective on physical reservoir computing with nanomagnetic devices","created":"2022-12-09","cited_by_count":12,"area":null},{"id":"2210.04763","title":"On the Forward Invariance of Neural ODEs","created":"2022-10-10","cited_by_count":11,"area":null},{"id":"2410.11648","title":"Efficient, Accurate and Stable Gradients for Neural ODEs","created":"2024-10-15","cited_by_count":9,"area":null},{"id":"2309.09222","title":"Bayesian Gaussian Process ODEs via Double Normalizing Flows","created":"2023-09-17","cited_by_count":7,"area":null},{"id":"2401.07387","title":"Noise-Aware Training of Neuromorphic Dynamic Device Networks","created":"2024-01-14","cited_by_count":5,"area":null},{"id":"2306.02192","title":"Correcting Auto-Differentiation in Neural-ODE Training","created":"2023-06-03","cited_by_count":3,"area":null},{"id":"2412.12161","title":"Discover physical concepts and equations with machine learning","created":"2024-12-11","cited_by_count":1,"area":null},{"id":"2504.08769","title":"High-order expansion of Neural Ordinary Differential Equations flows","created":"2025-04-02","cited_by_count":1,"area":null},{"id":"2601.15340","title":"Learning Nonlinear Heterogeneity in Physical Kolmogorov-Arnold Networks","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2405.07359","title":"Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation","created":"2024-05-12","cited_by_count":0,"area":null},{"id":"2410.14986","title":"NeuralMAG: Fast and Generalizable Micromagnetic Simulation with Deep Neural Nets","created":"2024-10-19","cited_by_count":0,"area":null},{"id":"2609.34070","title":"FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}