{"total":46,"rows":[{"id":"2306.00258","title":"Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior","created":"2023-06-01","cited_by_count":131,"area":null},{"id":"2304.07993","title":"In-Context Operator Learning with Data Prompts for Differential Equation Problems","created":"2023-04-17","cited_by_count":101,"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":"2401.07364","title":"PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws","created":"2024-01-14","cited_by_count":31,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2308.12716","title":"Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks","created":"2023-08-24","cited_by_count":18,"area":null},{"id":"2308.06672","title":"A practical PINN framework for multi-scale problems with multi-magnitude loss terms","created":"2023-08-13","cited_by_count":15,"area":null},{"id":"2405.12465","title":"A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains","created":"2024-05-21","cited_by_count":15,"area":null},{"id":"2502.00782","title":"Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation","created":"2025-02-02","cited_by_count":15,"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":"2404.00074","title":"A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations","created":"2024-03-28","cited_by_count":10,"area":null},{"id":"2502.00318","title":"Sub-Sequential Physics-Informed Learning with State Space Model","created":"2025-02-01","cited_by_count":10,"area":null},{"id":"2401.02810","title":"Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning","created":"2024-01-05","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":"2311.00578","title":"Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations","created":"2023-11-01","cited_by_count":8,"area":null},{"id":"2303.13634","title":"Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity","created":"2023-03-22","cited_by_count":7,"area":null},{"id":"2211.16190","title":"Physics Informed Neural Network for Dynamic Stress Prediction","created":"2022-11-28","cited_by_count":6,"area":null},{"id":"2303.12261","title":"Challenges and opportunities for machine learning in multiscale computational modeling","created":"2023-03-22","cited_by_count":6,"area":null},{"id":"2407.10836","title":"Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations","created":"2024-07-15","cited_by_count":5,"area":null},{"id":"2212.07723","title":"Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data","created":"2022-12-15","cited_by_count":4,"area":null},{"id":"2401.02363","title":"Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations","created":"2024-01-04","cited_by_count":4,"area":null},{"id":"2304.04854","title":"iPINNs: Incremental learning for Physics-informed neural networks","created":"2023-04-10","cited_by_count":3,"area":null},{"id":"2410.22796","title":"Solving Differential Equations with Constrained Learning","created":"2024-10-30","cited_by_count":3,"area":null},{"id":"2412.05719","title":"Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs","created":"2024-12-07","cited_by_count":3,"area":null},{"id":"2504.06327","title":"Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries","created":"2025-04-08","cited_by_count":3,"area":null},{"id":"2506.00731","title":"iPINNER: An Iterative Physics-Informed Neural Network with Ensemble Kalman Filter","created":"2025-05-31","cited_by_count":3,"area":null},{"id":"2509.09611","title":"ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance","created":"2025-09-11","cited_by_count":2,"area":null},{"id":"2605.31231","title":"A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials","created":"2026-05-29","cited_by_count":2,"area":null},{"id":"2412.09453","title":"Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics","created":"2024-12-12","cited_by_count":1,"area":null},{"id":"2504.15487","title":"Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence","created":"2025-04-21","cited_by_count":1,"area":null},{"id":"2509.19929","title":"Geometric Autoencoder Priors for Bayesian Inversion: Learn First Observe Later","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2601.00866","title":"A-PINN: Auxiliary Physics-informed Neural Networks for Structural Vibration Analysis in Continuous Euler-Bernoulli Beam","created":"2025-12-30","cited_by_count":1,"area":null},{"id":"2304.06234","title":"Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEs","created":"2023-04-13","cited_by_count":0,"area":null},{"id":"2401.11217","title":"A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant","created":"2024-01-20","cited_by_count":0,"area":null},{"id":"2501.07809","title":"Conformal mapping based Physics-informed neural networks for designing neutral inclusions","created":"2025-01-14","cited_by_count":0,"area":null},{"id":"2502.05669","title":"Rigid Body Adversarial Attacks","created":"2025-02-08","cited_by_count":0,"area":null},{"id":"2502.07415","title":"The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems","created":"2025-02-11","cited_by_count":0,"area":null},{"id":"2503.22396","title":"On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach","created":"2025-03-28","cited_by_count":0,"area":null},{"id":"2507.22370","title":"Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2508.00286","title":"Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem","created":"2025-08-01","cited_by_count":0,"area":null},{"id":"2509.07245","title":"IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2603.15194","title":"PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing","created":"2026-03-16","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":"2606.06171","title":"Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks","created":"2026-06-04","cited_by_count":0,"area":null},{"id":"2610.14008","title":"Generalized PINN-Based Inverse Estimation of Spatially Varying Heat Transfer Coefficients with Transfer Learning","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.14342","title":"Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}