{"total":110,"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":"2207.10289","title":"A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks","created":"2022-07-21","cited_by_count":236,"area":null},{"id":"2111.02801","title":"Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems","created":"2021-11-01","cited_by_count":222,"area":null},{"id":"2203.07404","title":"Respecting causality is all you need for training physics-informed neural networks","created":"2022-03-14","cited_by_count":156,"area":null},{"id":"2308.08468","title":"An Expert's Guide to Training Physics-informed Neural Networks","created":"2023-08-16","cited_by_count":126,"area":null},{"id":"2402.01868","title":"Challenges in Training PINNs: A Loss Landscape Perspective","created":"2024-02-02","cited_by_count":113,"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":"2207.02338","title":"Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling","created":"2022-07-05","cited_by_count":73,"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":"2407.17611","title":"Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks","created":"2024-07-24","cited_by_count":38,"area":null},{"id":"2406.19662","title":"Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems","created":"2024-06-28","cited_by_count":35,"area":null},{"id":"2404.07662","title":"PINNACLE: PINN Adaptive ColLocation and Experimental points selection","created":"2024-04-11","cited_by_count":32,"area":null},{"id":"2209.03984","title":"$\\Delta$-PINNs: physics-informed neural networks on complex geometries","created":"2022-09-08","cited_by_count":28,"area":null},{"id":"2110.03049","title":"Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training","created":"2021-10-06","cited_by_count":26,"area":null},{"id":"2211.05567","title":"Partial Differential Equations Meet Deep Neural Networks: A Survey","created":"2022-10-27","cited_by_count":25,"area":null},{"id":"2211.07377","title":"Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing","created":"2022-11-14","cited_by_count":24,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2311.06483","title":"Stacked networks improve physics-informed training: applications to neural networks and deep operator networks","created":"2023-11-11","cited_by_count":21,"area":null},{"id":"2405.15603","title":"Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks","created":"2024-05-24","cited_by_count":21,"area":null},{"id":"2204.11144","title":"Competitive Physics Informed Networks","created":"2022-04-23","cited_by_count":19,"area":null},{"id":"2408.01600","title":"Physics-Informed Geometry-Aware Neural Operator","created":"2024-08-02","cited_by_count":19,"area":null},{"id":"2303.01560","title":"Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal","created":"2023-03-02","cited_by_count":17,"area":null},{"id":"2205.01051","title":"RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks","created":"2022-05-02","cited_by_count":14,"area":null},{"id":"2205.13748","title":"Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture","created":"2022-05-27","cited_by_count":14,"area":null},{"id":"2304.03894","title":"A multifidelity approach to continual learning for physical systems","created":"2023-04-08","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":"2502.09346","title":"Machine learning for modelling unstructured grid data in computational physics: a review","created":"2025-02-13","cited_by_count":12,"area":null},{"id":"2501.19160","title":"Physics-Informed Representation Alignment for Sparse Radio-Map Reconstruction","created":"2025-01-31","cited_by_count":11,"area":null},{"id":"2302.13163","title":"Achieving High Accuracy with PINNs via Energy Natural Gradients","created":"2023-02-25","cited_by_count":10,"area":null},{"id":"2203.01514","title":"Physics-informed neural network solution of thermo-hydro-mechanical (THM) processes in porous media","created":"2022-03-03","cited_by_count":8,"area":null},{"id":"2210.05837","title":"A composable machine-learning approach for steady-state simulations on high-resolution grids","created":"2022-10-11","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":"2303.15849","title":"GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs","created":"2023-03-28","cited_by_count":6,"area":null},{"id":"2509.14198","title":"A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning","created":"2025-09-17","cited_by_count":6,"area":null},{"id":"2511.05452","title":"Self-adaptive weighting and sampling for physics-informed neural networks","created":"2025-11-07","cited_by_count":6,"area":null},{"id":"2210.12914","title":"A Novel Adaptive Causal Sampling Method for Physics-Informed Neural Networks","created":"2022-10-24","cited_by_count":5,"area":null},{"id":"2211.11396","title":"A Curriculum-Training-Based Strategy for Distributing Collocation Points during Physics-Informed Neural Network Training","created":"2022-11-21","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":"2405.11208","title":"Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation","created":"2024-05-18","cited_by_count":5,"area":null},{"id":"2206.01545","title":"Mesh-free Eulerian Physics-Informed Neural Networks","created":"2022-06-03","cited_by_count":4,"area":null},{"id":"2209.05315","title":"Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network","created":"2022-09-09","cited_by_count":4,"area":null},{"id":"2210.10646","title":"Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks","created":"2022-10-19","cited_by_count":4,"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":"2302.01518","title":"LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry","created":"2023-02-03","cited_by_count":4,"area":null},{"id":"2402.03153","title":"Learning solutions of parametric Navier-Stokes with physics-informed neural networks","created":"2024-02-05","cited_by_count":4,"area":null},{"id":"2406.05108","title":"Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population Dynamics","created":"2024-06-07","cited_by_count":4,"area":null},{"id":"2409.20383","title":"Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis","created":"2024-09-30","cited_by_count":4,"area":null},{"id":"2501.11222","title":"An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks","created":"2025-01-20","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":"2110.03780","title":"A composable autoencoder-based iterative algorithm for accelerating numerical simulations","created":"2021-10-07","cited_by_count":3,"area":null},{"id":"2212.11776","title":"Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference","created":"2022-12-22","cited_by_count":3,"area":null},{"id":"2302.10891","title":"An Implicit GNN Solver for Poisson-like problems","created":"2023-02-06","cited_by_count":3,"area":null},{"id":"2308.06447","title":"A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing","created":"2023-08-12","cited_by_count":3,"area":null},{"id":"2310.16491","title":"TSONN: Time-stepping-oriented neural network for solving partial differential equations","created":"2023-10-25","cited_by_count":3,"area":null},{"id":"2404.12282","title":"Investigating Guiding Information for Adaptive Collocation Point Sampling in PINNs","created":"2024-04-18","cited_by_count":3,"area":null},{"id":"2406.02581","title":"Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data","created":"2024-05-30","cited_by_count":3,"area":null},{"id":"2406.04170","title":"Element-wise Multiplication Based Deeper Physics-Informed Neural Networks","created":"2024-06-06","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":"2505.01078","title":"Integration Matters for Learning PDEs with Backward SDEs","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2505.06459","title":"Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles","created":"2025-05-09","cited_by_count":3,"area":null},{"id":"2511.14348","title":"Enforcing hidden physics in physics-informed neural networks","created":"2025-11-18","cited_by_count":3,"area":null},{"id":"2210.14320","title":"FO-PINNs: A First-Order formulation for Physics Informed Neural Networks","created":"2022-10-25","cited_by_count":2,"area":null},{"id":"2311.16167","title":"Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE","created":"2023-11-14","cited_by_count":2,"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":"2408.14734","title":"General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations","created":"2024-08-27","cited_by_count":2,"area":null},{"id":"2411.19632","title":"PACMANN: Point Adaptive Collocation Method for Artificial Neural Networks","created":"2024-11-29","cited_by_count":2,"area":null},{"id":"2501.07700","title":"Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method","created":"2025-01-13","cited_by_count":2,"area":null},{"id":"2504.16447","title":"Node Assigned physics-informed neural networks for thermal-hydraulic system simulation: CVH/FL module","created":"2025-04-23","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":"2507.21437","title":"PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems","created":"2025-07-29","cited_by_count":2,"area":null},{"id":"2510.23501","title":"Training Deep Physics-Informed Kolmogorov-Arnold Networks","created":"2025-10-27","cited_by_count":2,"area":null},{"id":"2306.05554","title":"Simulation and Prediction of Countercurrent Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural Networks","created":"2023-05-06","cited_by_count":1,"area":null},{"id":"2403.07507","title":"Reconstructions of Jupiter's magnetic field using physics informed neural networks","created":"2024-03-12","cited_by_count":1,"area":null},{"id":"2408.10011","title":"PinnDE: Physics-Informed Neural Networks for Solving Differential Equations","created":"2024-08-19","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":"2507.10983","title":"Physics-Informed Neural Networks For Semiconductor Film Deposition: A Review","created":"2025-07-15","cited_by_count":1,"area":null},{"id":"2602.01397","title":"Nonlinear model reduction for transport-dominated problems","created":"2026-02-01","cited_by_count":1,"area":null},{"id":"2602.02547","title":"naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement","created":"2026-01-30","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":"2306.08293","title":"Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm","created":"2023-06-14","cited_by_count":0,"area":null},{"id":"2308.08873","title":"Enhancing Convergence Speed with Feature-Enforcing Physics-Informed Neural Networks: Utilizing Boundary Conditions as Prior Knowledge for Faster Convergence","created":"2023-08-17","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":"2407.07358","title":"SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks","created":"2024-07-10","cited_by_count":0,"area":null},{"id":"2409.12877","title":"Physics aware machine learning for micromagnetic energy minimization: recent algorithmic developments","created":"2024-09-19","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":"2505.12353","title":"Importance Sampling for Nonlinear Models","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2506.06308","title":"Scientific machine learning in Hydrology: a unified perspective","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2507.08121","title":"Quasi-Random Physics-informed Neural Networks","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2507.09757","title":"Energy Dissipation Rate Guided Adaptive Sampling for Physics-Informed Neural Networks: Resolving Surface-Bulk Dynamics in Allen-Cahn Systems","created":"2025-07-13","cited_by_count":0,"area":null},{"id":"2509.21123","title":"Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2510.24026","title":"Efficient Global-Local Fusion Sampling for Physics-Informed Neural Networks","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2511.22819","title":"Resolving Sharp Gradients of Unstable Singularities to Machine Precision via Neural Networks","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2512.10873","title":"Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2512.17607","title":"More Consistent Accuracy PINN via Alternating Easy-Hard Training","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2602.01176","title":"Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations","created":"2026-02-01","cited_by_count":0,"area":null},{"id":"2603.06761","title":"Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets","created":"2026-03-06","cited_by_count":0,"area":null}],"next_cursor":"100"}