{"total":52,"rows":[{"id":"2201.05624","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","created":"2022-01-14","cited_by_count":608,"area":null},{"id":"2106.14103","title":"PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs","created":"2021-06-26","cited_by_count":98,"area":null},{"id":"2110.15832","title":"CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method","created":"2021-10-29","cited_by_count":93,"area":null},{"id":"2211.08064","title":"Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications","created":"2022-11-15","cited_by_count":92,"area":null},{"id":"2306.08827","title":"PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs","created":"2023-06-15","cited_by_count":87,"area":null},{"id":"2201.09113","title":"Predicting Physics in Mesh-reduced Space with Temporal Attention","created":"2022-01-22","cited_by_count":84,"area":null},{"id":"2410.13228","title":"From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning","created":"2024-10-17","cited_by_count":73,"area":null},{"id":"2106.04781","title":"Encoding physics to learn reaction-diffusion processes","created":"2021-06-09","cited_by_count":61,"area":null},{"id":"2110.11286","title":"One-Shot Transfer Learning of Physics-Informed Neural Networks","created":"2021-10-21","cited_by_count":49,"area":null},{"id":"2205.03990","title":"Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics","created":"2022-05-09","cited_by_count":44,"area":null},{"id":"2407.17611","title":"Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks","created":"2024-07-24","cited_by_count":38,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2210.14044","title":"SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain","created":"2022-10-25","cited_by_count":16,"area":null},{"id":"2205.08321","title":"Finite Element Method-enhanced Neural Network for Forward and Inverse Problems","created":"2022-05-17","cited_by_count":13,"area":null},{"id":"2411.00040","title":"P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics","created":"2024-10-29","cited_by_count":13,"area":null},{"id":"2501.06572","title":"Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities","created":"2025-01-11","cited_by_count":11,"area":null},{"id":"2208.01462","title":"Physics-informed Deep Super-resolution for Spatiotemporal Data","created":"2022-08-02","cited_by_count":9,"area":null},{"id":"2406.03172","title":"Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)","created":"2024-06-05","cited_by_count":9,"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":"2210.12177","title":"An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator","created":"2022-10-21","cited_by_count":7,"area":null},{"id":"2205.05390","title":"AutoKE: An automatic knowledge embedding framework for scientific machine learning","created":"2022-05-11","cited_by_count":6,"area":null},{"id":"2309.00305","title":"Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties","created":"2023-09-01","cited_by_count":6,"area":null},{"id":"2402.17157","title":"Generative Learning for Forecasting the Dynamics of Complex Systems","created":"2024-02-27","cited_by_count":6,"area":null},{"id":"2502.11279","title":"Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards","created":"2025-02-16","cited_by_count":6,"area":null},{"id":"2311.12915","title":"Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics","created":"2023-11-21","cited_by_count":4,"area":null},{"id":"2407.05232","title":"PAPM: A Physics-aware Proxy Model for Process Systems","created":"2024-07-07","cited_by_count":4,"area":null},{"id":"2412.20962","title":"Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction","created":"2024-12-30","cited_by_count":4,"area":null},{"id":"2509.01234","title":"RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations","created":"2025-09-01","cited_by_count":4,"area":null},{"id":"2301.13262","title":"Temporal Consistency Loss for Physics-Informed Neural Networks","created":"2023-01-30","cited_by_count":3,"area":null},{"id":"2302.10779","title":"Physics-Informed Long Short-Term Memory for Forecasting and Reconstruction of Chaos","created":"2023-02-03","cited_by_count":3,"area":null},{"id":"2503.10021","title":"DGNN: A Neural PDE Solver Induced by Discontinuous Galerkin Methods","created":"2025-03-13","cited_by_count":3,"area":null},{"id":"2307.12304","title":"Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics","created":"2023-07-23","cited_by_count":2,"area":null},{"id":"2407.16800","title":"Wasserstein Distributionally Robust Shallow Convex Neural Networks","created":"2024-07-23","cited_by_count":2,"area":null},{"id":"2301.12118","title":"Physics-informed Neural Network: The Effect of Reparameterization in Solving Differential Equations","created":"2023-01-28","cited_by_count":1,"area":null},{"id":"2402.11126","title":"Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2410.04193","title":"Parametric Taylor series based latent dynamics identification neural networks","created":"2024-10-05","cited_by_count":1,"area":null},{"id":"2507.01975","title":"Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2508.00734","title":"Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems","created":"2025-08-01","cited_by_count":1,"area":null},{"id":"2101.03701","title":"Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach","created":"2021-01-11","cited_by_count":0,"area":null},{"id":"2106.12694","title":"Long short-term relevance learning","created":"2021-06-21","cited_by_count":0,"area":null},{"id":"2308.08655","title":"Physics Informed Recurrent Neural Networks for Seismic Response Evaluation of Nonlinear Systems","created":"2023-08-16","cited_by_count":0,"area":null},{"id":"2402.17992","title":"Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures","created":"2024-02-28","cited_by_count":0,"area":null},{"id":"2405.09021","title":"Deep Learning in Earthquake Engineering: A Comprehensive Review","created":"2024-05-15","cited_by_count":0,"area":null},{"id":"2503.07258","title":"MC-GRU:a Multi-Channel GRU network for generalized nonlinear structural response prediction across structures","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2506.05918","title":"Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2506.20799","title":"Structural System Identification via Validation and Adaptation","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2511.21276","title":"A physics-informed U-Net-LSTM network for nonlinear structural response under seismic excitation","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2512.06714","title":"A Novel Deep Neural Network Architecture for Real-Time Water Demand Forecasting","created":"2025-12-07","cited_by_count":0,"area":null},{"id":"2603.12012","title":"Deep Learning-Based Metamodeling of Nonlinear Stochastic Dynamic Systems under Parametric and Predictive Uncertainty","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2604.01587","title":"Variational LSTM with Augmented Inputs: Nonlinear Response History Metamodeling with Aleatoric and Epistemic Uncertainty","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2609.09912","title":"Development and Validation of a Physics-Guided Machine Learning Extrapolation Framework Using a Classical Transient Diffusion Benchmark","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.19871","title":"Physical knowledge on historical data matters more than enforcing physical constraints on the forecast","created":"2026-09-17","cited_by_count":0,"area":null}],"next_cursor":null}