{"total":42,"rows":[{"id":"2107.10879","title":"Discovering Sparse Interpretable Dynamics from Partial Observations","created":"2021-07-22","cited_by_count":25,"area":null},{"id":"2106.00008","title":"Robust discovery of partial differential equations in complex situations","created":"2021-05-31","cited_by_count":21,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2304.07599","title":"Learning in latent spaces improves the predictive accuracy of deep neural operators","created":"2023-04-15","cited_by_count":19,"area":null},{"id":"2105.02368","title":"Physics-informed Spline Learning for Nonlinear Dynamics Discovery","created":"2021-05-05","cited_by_count":18,"area":null},{"id":"2208.03322","title":"Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion","created":"2022-08-05","cited_by_count":16,"area":null},{"id":"2309.04699","title":"Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited Data","created":"2023-09-09","cited_by_count":16,"area":null},{"id":"2206.12901","title":"Noise-aware Physics-informed Machine Learning for Robust PDE Discovery","created":"2022-06-26","cited_by_count":14,"area":null},{"id":"2210.04338","title":"A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks","created":"2022-10-09","cited_by_count":14,"area":null},{"id":"2102.02617","title":"A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate","created":"2021-02-04","cited_by_count":13,"area":null},{"id":"2107.12780","title":"Physics-constrained Deep Learning for Robust Inverse ECG Modeling","created":"2021-07-26","cited_by_count":12,"area":null},{"id":"2201.10215","title":"Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models","created":"2022-01-25","cited_by_count":10,"area":null},{"id":"2111.07671","title":"NeuralPDE: Modelling Dynamical Systems from Data","created":"2021-11-15","cited_by_count":9,"area":null},{"id":"2306.05805","title":"DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data","created":"2023-06-09","cited_by_count":8,"area":null},{"id":"2107.06991","title":"Physics-informed generative neural network: an application to troposphere temperature prediction","created":"2021-07-08","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":"2112.01254","title":"Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks","created":"2021-12-02","cited_by_count":5,"area":null},{"id":"2310.05387","title":"Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels","created":"2023-10-09","cited_by_count":5,"area":null},{"id":"2505.10930","title":"Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2101.07023","title":"Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification","created":"2021-01-18","cited_by_count":4,"area":null},{"id":"2302.12235","title":"Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows","created":"2023-02-23","cited_by_count":4,"area":null},{"id":"2106.00009","title":"Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data","created":"2021-05-31","cited_by_count":3,"area":null},{"id":"2107.07040","title":"Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations","created":"2021-07-08","cited_by_count":3,"area":null},{"id":"2112.09182","title":"Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning","created":"2021-12-16","cited_by_count":3,"area":null},{"id":"2402.03747","title":"An invariance constrained deep learning network for PDE discovery","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2105.00400","title":"Model discovery in the sparse sampling regime","created":"2021-05-02","cited_by_count":2,"area":null},{"id":"2108.05385","title":"Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems","created":"2021-08-11","cited_by_count":2,"area":null},{"id":"2112.11360","title":"Multigoal-oriented dual-weighted-residual error estimation using deep neural networks","created":"2021-12-21","cited_by_count":2,"area":null},{"id":"2405.08613","title":"GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations","created":"2024-05-14","cited_by_count":2,"area":null},{"id":"2405.18311","title":"Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2512.23761","title":"Learning Coupled System Dynamics under Incomplete Physical Constraints and Missing Data","created":"2025-12-28","cited_by_count":2,"area":null},{"id":"2105.01838","title":"Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks","created":"2021-05-05","cited_by_count":1,"area":null},{"id":"2106.04886","title":"Fully differentiable model discovery","created":"2021-06-09","cited_by_count":1,"area":null},{"id":"2108.05940","title":"ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting","created":"2021-08-12","cited_by_count":1,"area":null},{"id":"2110.08382","title":"A Neural Network Ensemble Approach to System Identification","created":"2021-10-15","cited_by_count":1,"area":null},{"id":"2604.19296","title":"Debiased neural operators for estimating functionals","created":"2026-04-21","cited_by_count":1,"area":null},{"id":"2110.01408","title":"Paradigm Shift Through the Integration of Physical Methodology and Data Science","created":"2021-09-30","cited_by_count":0,"area":null},{"id":"2208.08276","title":"Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition","created":"2022-08-16","cited_by_count":0,"area":null},{"id":"2306.12413","title":"Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control","created":"2023-06-21","cited_by_count":0,"area":null},{"id":"2512.19936","title":"GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2603.22380","title":"Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data","created":"2026-03-23","cited_by_count":0,"area":null},{"id":"2605.11524","title":"EqOD: Symmetry-Informed Stability Selection for PDE Identification","created":"2026-05-12","cited_by_count":0,"area":null}],"next_cursor":null}