{"total":20,"rows":[{"id":"2207.05748","title":"Physics-Informed Deep Neural Operator Networks","created":"2022-07-08","cited_by_count":113,"area":null},{"id":"2310.19809","title":"MgNO: Efficient Parameterization of Linear Operators via Multigrid","created":"2023-10-16","cited_by_count":40,"area":null},{"id":"2305.00478","title":"Domain Agnostic Fourier Neural Operators","created":"2023-04-30","cited_by_count":38,"area":null},{"id":"2212.14365","title":"INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation","created":"2022-12-29","cited_by_count":28,"area":null},{"id":"2401.06070","title":"Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses","created":"2024-01-11","cited_by_count":18,"area":null},{"id":"2504.03503","title":"Operator Learning: A Statistical Perspective","created":"2025-04-04","cited_by_count":17,"area":null},{"id":"2303.07647","title":"Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review","created":"2023-03-14","cited_by_count":15,"area":null},{"id":"2405.09572","title":"Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing","created":"2024-05-13","cited_by_count":14,"area":null},{"id":"2307.04569","title":"Interpreting and generalizing deep learning in physics-based problems with functional linear models","created":"2023-07-10","cited_by_count":9,"area":null},{"id":"2310.03745","title":"Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields","created":"2023-09-11","cited_by_count":7,"area":null},{"id":"2602.17750","title":"Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models","created":"2026-02-19","cited_by_count":6,"area":null},{"id":"2301.02378","title":"Deep learning for full-field ultrasonic characterization","created":"2023-01-06","cited_by_count":5,"area":null},{"id":"2403.18597","title":"Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements","created":"2024-03-27","cited_by_count":4,"area":null},{"id":"2510.05433","title":"Physics-Informed Machine Learning in Biomedical Science and Engineering","created":"2025-10-06","cited_by_count":4,"area":null},{"id":"2301.04540","title":"Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures","created":"2023-01-11","cited_by_count":3,"area":null},{"id":"2508.20650","title":"Self-composing neural operators for high-frequency and multiscale PDE surrogates","created":"2025-08-28","cited_by_count":3,"area":null},{"id":"2308.15697","title":"Segmenting mechanically heterogeneous domains via unsupervised learning","created":"2023-08-30","cited_by_count":2,"area":null},{"id":"2507.05498","title":"Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)","created":"2025-07-07","cited_by_count":2,"area":null},{"id":"2607.18734","title":"Uncertainty quantification in mechanics: A unified Bayesian perspective","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.22755","title":"pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting","created":"2026-07-23","cited_by_count":0,"area":null}],"next_cursor":null}