{"total":121,"rows":[{"id":"2312.14688","title":"A Mathematical Guide to Operator Learning","created":"2023-12-22","cited_by_count":76,"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":"2402.00326","title":"PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks","created":"2024-02-01","cited_by_count":72,"area":null},{"id":"2502.00604","title":"Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective","created":"2025-02-02","cited_by_count":56,"area":null},{"id":"2405.13998","title":"CViT: Continuous Vision Transformer for Operator Learning","created":"2024-05-22","cited_by_count":51,"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":"2405.14369","title":"RoPINN: Region Optimized Physics-Informed Neural Networks","created":"2024-05-23","cited_by_count":26,"area":null},{"id":"2407.18373","title":"Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficent-KAN and WAV-KAN","created":"2024-07-25","cited_by_count":25,"area":null},{"id":"2311.04293","title":"Lie Point Symmetry and Physics Informed Networks","created":"2023-11-07","cited_by_count":24,"area":null},{"id":"2407.07873","title":"Dynamical Measure Transport and Neural PDE Solvers for Sampling","created":"2024-07-10","cited_by_count":20,"area":null},{"id":"2402.19242","title":"Derivative-enhanced Deep Operator Network","created":"2024-02-29","cited_by_count":19,"area":null},{"id":"2403.15989","title":"Knowledge-guided Machine Learning: Current Trends and Future Prospects","created":"2024-03-24","cited_by_count":14,"area":null},{"id":"2507.08972","title":"Simulating Three-dimensional Turbulence with Physics-informed Neural Networks","created":"2025-07-11","cited_by_count":14,"area":null},{"id":"2503.06320","title":"Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble","created":"2025-03-08","cited_by_count":13,"area":null},{"id":"2401.08667","title":"Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective","created":"2024-01-05","cited_by_count":12,"area":null},{"id":"2410.01990","title":"Deep Learning Alternatives of the Kolmogorov Superposition Theorem","created":"2024-10-02","cited_by_count":12,"area":null},{"id":"2506.10973","title":"Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning","created":"2025-06-12","cited_by_count":12,"area":null},{"id":"2412.05994","title":"PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations","created":"2024-12-08","cited_by_count":11,"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":"2403.12438","title":"Precise-Physics Driven Text-to-3D Generation","created":"2024-03-19","cited_by_count":10,"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":"2403.06342","title":"Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation","created":"2024-03-10","cited_by_count":8,"area":null},{"id":"2404.17789","title":"BiLO: Bilevel Local Operator Learning for PDE Inverse Problems","created":"2024-04-27","cited_by_count":8,"area":null},{"id":"2510.06646","title":"The False Promise of Zero-Shot Super-Resolution in Machine-Learned Operators","created":"2025-10-08","cited_by_count":8,"area":null},{"id":"2502.00803","title":"ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks","created":"2025-02-02","cited_by_count":6,"area":null},{"id":"2504.01093","title":"Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings","created":"2025-04-01","cited_by_count":6,"area":null},{"id":"2312.03243","title":"Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect","created":"2023-12-06","cited_by_count":5,"area":null},{"id":"2402.08367","title":"RBF-PINN: Non-Fourier Positional Embedding in Physics-Informed Neural Networks","created":"2024-02-13","cited_by_count":5,"area":null},{"id":"2410.05281","title":"Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials","created":"2024-09-23","cited_by_count":5,"area":null},{"id":"2509.18483","title":"Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints","created":"2025-09-23","cited_by_count":5,"area":null},{"id":"2602.19475","title":"Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction","created":"2026-02-23","cited_by_count":5,"area":null},{"id":"2408.14951","title":"Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems","created":"2024-08-27","cited_by_count":4,"area":null},{"id":"2501.18527","title":"Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?","created":"2025-01-30","cited_by_count":4,"area":null},{"id":"2502.11942","title":"Sharp-PINNs: staggered hard-constrained physics-informed neural networks for phase field modelling of corrosion","created":"2025-02-17","cited_by_count":4,"area":null},{"id":"2505.18131","title":"Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement","created":"2025-05-23","cited_by_count":4,"area":null},{"id":"2510.08924","title":"AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2601.07760","title":"Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning","created":"2026-01-12","cited_by_count":4,"area":null},{"id":"2311.15940","title":"Physics-informed neural networks for transformed geometries and manifolds","created":"2023-11-27","cited_by_count":3,"area":null},{"id":"2404.08754","title":"Computing distances and means on manifolds with a metric-constrained Eikonal approach","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2503.17402","title":"Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networks","created":"2025-03-19","cited_by_count":3,"area":null},{"id":"2505.01078","title":"Integration Matters for Learning PDEs with Backward SDEs","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2508.12569","title":"Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems","created":"2025-08-18","cited_by_count":3,"area":null},{"id":"2509.04966","title":"Neuro-Spectral Architectures for Causal Physics-Informed Networks","created":"2025-09-05","cited_by_count":3,"area":null},{"id":"2402.06955","title":"Feature Mapping in Physics-Informed Neural Networks (PINNs)","created":"2024-02-10","cited_by_count":2,"area":null},{"id":"2412.09752","title":"A Quasilinear Algorithm for Computing Higher-Order Derivatives of Deep Feed-Forward Neural Networks","created":"2024-12-12","cited_by_count":2,"area":null},{"id":"2505.03382","title":"Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models","created":"2025-05-06","cited_by_count":2,"area":null},{"id":"2505.20300","title":"FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design","created":"2025-05-10","cited_by_count":2,"area":null},{"id":"2506.16443","title":"Leveraging Influence Functions for Resampling Data in Physics-Informed Neural Networks","created":"2025-06-19","cited_by_count":2,"area":null},{"id":"2601.06244","title":"Hard Constraint Projection in a Physics Informed Neural Network","created":"2026-01-09","cited_by_count":2,"area":null},{"id":"2604.18481","title":"Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle","created":"2026-04-20","cited_by_count":2,"area":null},{"id":"2604.23528","title":"When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions","created":"2026-04-26","cited_by_count":2,"area":null},{"id":"2606.09949","title":"Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training","created":"2026-06-08","cited_by_count":2,"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":"2406.02336","title":"Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2406.02645","title":"Astral: training physics-informed neural networks with error majorants","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2406.09071","title":"FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames","created":"2024-06-07","cited_by_count":1,"area":null},{"id":"2406.10534","title":"Finite-difference-informed graph network for solving steady-state incompressible flows on block-structured grids","created":"2024-06-15","cited_by_count":1,"area":null},{"id":"2410.05860","title":"MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning","created":"2024-10-08","cited_by_count":1,"area":null},{"id":"2411.08326","title":"Neural Conjugate Flows: Physics-informed architectures with flow structure","created":"2024-11-13","cited_by_count":1,"area":null},{"id":"2502.05044","title":"Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2502.17597","title":"Unraveling particle dark matter with Physics-Informed Neural Networks","created":"2025-02-24","cited_by_count":1,"area":null},{"id":"2504.09804","title":"BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2508.02537","title":"Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs","created":"2025-08-04","cited_by_count":1,"area":null},{"id":"2510.10483","title":"Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations","created":"2025-10-12","cited_by_count":1,"area":null},{"id":"2511.11048","title":"PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI","created":"2025-11-14","cited_by_count":1,"area":null},{"id":"2512.15083","title":"Neural Modular Physics for Elastic Simulation","created":"2025-12-17","cited_by_count":1,"area":null},{"id":"2512.20270","title":"Optimality-Informed Neural Networks for Solving Parametric Optimization Problems","created":"2025-12-23","cited_by_count":1,"area":null},{"id":"2601.17562","title":"Sparse RBF Networks for PDEs and nonlocal equations: function space theory, operator calculus, and training algorithms","created":"2026-01-24","cited_by_count":1,"area":null},{"id":"2603.04827","title":"Multilevel Training for Kolmogorov Arnold Networks","created":"2026-03-05","cited_by_count":1,"area":null},{"id":"2603.09693","title":"Physics-informed neural operator for parametric phase-field modelling of interfacial degradation and microstructural evolution","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2605.08408","title":"AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.10136","title":"Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2605.11316","title":"Error whitening: Why Gauss-Newton outperforms Newton","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2605.24278","title":"Fourier Feature Pyramids for Physics-Informed Neural Networks","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2606.07146","title":"Decision-Aware Evaluation of Physics-Informed Surrogates","created":"2026-06-05","cited_by_count":1,"area":null},{"id":"2607.07425","title":"Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design","created":"2026-07-08","cited_by_count":1,"area":null},{"id":"2609.24947","title":"Learning Physics from an Imperfect Ancestor","created":"2026-09-21","cited_by_count":1,"area":null},{"id":"2310.06157","title":"Manifold-augmented Eikonal Equations: Geodesic Distances and Flows on Differentiable Manifolds","created":"2023-10-09","cited_by_count":0,"area":null},{"id":"2402.17232","title":"Two-scale Neural Networks for Partial Differential Equations with Small Parameters","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2406.09194","title":"Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias","created":"2024-06-13","cited_by_count":0,"area":null},{"id":"2409.09207","title":"FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition","created":"2024-09-13","cited_by_count":0,"area":null},{"id":"2410.06219","title":"Gaussian Variational Schemes on Bounded and Unbounded Domains","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2410.07527","title":"Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics","created":"2024-10-10","cited_by_count":0,"area":null},{"id":"2502.15913","title":"Connecting the geometry and dynamics of many-body complex systems with message passing neural operators","created":"2025-02-21","cited_by_count":0,"area":null},{"id":"2503.00213","title":"An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation","created":"2025-02-28","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":"2504.21328","title":"Multi-level datasets training method in Physics-Informed Neural Networks","created":"2025-04-30","cited_by_count":0,"area":null},{"id":"2505.02258","title":"Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks","created":"2025-04-28","cited_by_count":0,"area":null},{"id":"2505.10919","title":"A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2505.21723","title":"Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2507.09330","title":"WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs with Physics-Informed Neural Networks","created":"2025-07-12","cited_by_count":0,"area":null},{"id":"2508.04595","title":"Improved Training Strategies for Physics-Informed Neural Networks using Real Experimental Data in Aluminum Spot Welding","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2508.13216","title":"Strategies for training point distributions in physics-informed neural networks","created":"2025-08-17","cited_by_count":0,"area":null},{"id":"2509.02571","title":"Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2509.14442","title":"Indoor Airflow Imaging Using Physics-Informed Background-Oriented Schlieren Tomography","created":"2025-09-17","cited_by_count":0,"area":null},{"id":"2511.08314","title":"Improving the accuracy and generalizability of molecular property regression models with a substructure-substitution-rule-informed framework","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2511.23449","title":"Physics-Informed Neural Networks for Thermophysical Property Retrieval","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2512.13532","title":"Adaptive Sampling for Hydrodynamic Stability","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2512.16175","title":"Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2602.14663","title":"Pseudo-differential-enhanced physics-informed neural networks","created":"2026-02-16","cited_by_count":0,"area":null}],"next_cursor":"100"}