{"total":18,"rows":[{"id":"2212.14319","title":"Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients","created":"2022-12-29","cited_by_count":17,"area":null},{"id":"2309.10656","title":"A spectrum of physics-informed Gaussian processes for regression in engineering","created":"2023-09-19","cited_by_count":14,"area":null},{"id":"2201.13145","title":"Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems","created":"2022-01-31","cited_by_count":11,"area":null},{"id":"2101.11513","title":"State estimation with limited sensors -- A deep learning based approach","created":"2021-01-27","cited_by_count":7,"area":null},{"id":"2109.00538","title":"Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems","created":"2021-09-01","cited_by_count":7,"area":null},{"id":"2204.06362","title":"A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic","created":"2022-04-13","cited_by_count":7,"area":null},{"id":"2310.20425","title":"Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications","created":"2023-10-31","cited_by_count":7,"area":null},{"id":"2109.10681","title":"A Latent Restoring Force Approach to Nonlinear System Identification","created":"2021-09-22","cited_by_count":6,"area":null},{"id":"2110.13809","title":"A deep learning based surrogate model for stochastic simulators","created":"2021-10-24","cited_by_count":6,"area":null},{"id":"2309.11875","title":"Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes","created":"2023-09-21","cited_by_count":5,"area":null},{"id":"2103.15636","title":"Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system","created":"2021-03-29","cited_by_count":4,"area":null},{"id":"2105.04979","title":"Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis","created":"2021-05-11","cited_by_count":4,"area":null},{"id":"2303.00187","title":"On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniques","created":"2023-03-01","cited_by_count":3,"area":null},{"id":"2503.09418","title":"Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions","created":"2025-03-12","cited_by_count":2,"area":null},{"id":"2506.14925","title":"Digital twin for virtual sensing of ferry quays via a Gaussian Process Latent Force Model","created":"2025-06-17","cited_by_count":0,"area":null},{"id":"2511.00100","title":"Deep recurrent-convolutional neural network learning and physics Kalman filtering comparison in dynamic load identification","created":"2025-10-30","cited_by_count":0,"area":null},{"id":"2601.05297","title":"Machine learning assisted state prediction of misspecified linear dynamical system via modal reduction","created":"2026-01-08","cited_by_count":0,"area":null},{"id":"2603.21678","title":"CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis","created":"2026-03-23","cited_by_count":0,"area":null}],"next_cursor":null}