{"total":93,"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":"2308.08468","title":"An Expert's Guide to Training Physics-informed Neural Networks","created":"2023-08-16","cited_by_count":126,"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":"2402.00326","title":"PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks","created":"2024-02-01","cited_by_count":72,"area":null},{"id":"2306.15969","title":"Separable Physics-Informed Neural Networks","created":"2023-06-28","cited_by_count":66,"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":"2307.08934","title":"Multi-stage Neural Networks: Function Approximator of Machine Precision","created":"2023-07-18","cited_by_count":48,"area":null},{"id":"2310.05963","title":"CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics","created":"2023-09-13","cited_by_count":46,"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":"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":"2202.09340","title":"Learning Physics-Informed Neural Networks without Stacked Back-propagation","created":"2022-02-18","cited_by_count":23,"area":null},{"id":"2309.15421","title":"Deep Learning in Deterministic Computational Mechanics","created":"2023-09-27","cited_by_count":21,"area":null},{"id":"2506.06440","title":"Vid2Sim: Generalizable, Video-based Reconstruction of Appearance, Geometry and Physics for Mesh-free Simulation","created":"2025-06-06","cited_by_count":20,"area":null},{"id":"2208.04856","title":"Fully probabilistic deep models for forward and inverse problems in parametric PDEs","created":"2022-08-09","cited_by_count":19,"area":null},{"id":"2311.15283","title":"Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs","created":"2023-11-26","cited_by_count":19,"area":null},{"id":"2302.04954","title":"Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains","created":"2023-02-09","cited_by_count":14,"area":null},{"id":"2408.13101","title":"Functional Tensor Decompositions for Physics-Informed Neural Networks","created":"2024-08-23","cited_by_count":13,"area":null},{"id":"2411.08378","title":"Physics Informed Distillation for Diffusion Models","created":"2024-11-13","cited_by_count":13,"area":"diffusion"},{"id":"2308.09858","title":"Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks","created":"2023-08-18","cited_by_count":12,"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":"2310.05227","title":"Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology","created":"2023-10-08","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":"2405.14099","title":"Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations","created":"2024-05-23","cited_by_count":9,"area":null},{"id":"2212.08965","title":"Physics-informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media","created":"2022-12-17","cited_by_count":8,"area":null},{"id":"2301.11040","title":"Random Grid Neural Processes for Parametric Partial Differential Equations","created":"2023-01-26","cited_by_count":8,"area":null},{"id":"2309.07672","title":"Physics-constrained robust learning of open-form partial differential equations from limited and noisy data","created":"2023-09-14","cited_by_count":8,"area":null},{"id":"2405.12380","title":"Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem","created":"2024-05-20","cited_by_count":8,"area":null},{"id":"2312.09820","title":"On the locality of local neural operator in learning fluid dynamics","created":"2023-12-15","cited_by_count":6,"area":null},{"id":"2601.14517","title":"Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators","created":"2026-01-20","cited_by_count":6,"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":"2307.02766","title":"Temporal Difference Learning for High-Dimensional PIDEs with Jumps","created":"2023-07-06","cited_by_count":5,"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":"2402.09730","title":"DOF: Accelerating High-order Differential Operators with Forward Propagation","created":"2024-02-15","cited_by_count":5,"area":null},{"id":"2403.13850","title":"Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance","created":"2024-03-18","cited_by_count":5,"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":"2407.11253","title":"Separable Operator Networks","created":"2024-07-15","cited_by_count":5,"area":null},{"id":"2511.08310","title":"NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos","created":"2025-11-11","cited_by_count":5,"area":null},{"id":"2601.19091","title":"Out-of-Distribution Generalization for Neural Physics Solvers","created":"2026-01-27","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":"2212.02861","title":"RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network","created":"2022-12-06","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":"2305.12433","title":"ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations","created":"2023-05-21","cited_by_count":4,"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":"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":"2211.12042","title":"Robustness of Physics-Informed Neural Networks to Noise in Sensor Data","created":"2022-11-22","cited_by_count":3,"area":null},{"id":"2308.09571","title":"Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations","created":"2023-08-18","cited_by_count":3,"area":null},{"id":"2310.04788","title":"PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations","created":"2023-10-07","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":"2406.00227","title":"ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis","created":"2024-05-31","cited_by_count":3,"area":null},{"id":"2407.01560","title":"3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation","created":"2024-05-07","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":"2511.14348","title":"Enforcing hidden physics in physics-informed neural networks","created":"2025-11-18","cited_by_count":3,"area":null},{"id":"2310.11789","title":"PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack","created":"2023-10-18","cited_by_count":2,"area":null},{"id":"2311.00259","title":"Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks","created":"2023-11-01","cited_by_count":2,"area":null},{"id":"2504.21501","title":"Deep Learning Optimization Using Self-Adaptive Weighted Auxiliary Variables","created":"2025-04-30","cited_by_count":2,"area":null},{"id":"2507.17019","title":"Bayesian BiLO: Bilevel Local Operator Learning for Efficient Uncertainty Quantification of Bayesian PDE Inverse Problems with Low-Rank Adaptation","created":"2025-07-22","cited_by_count":2,"area":null},{"id":"2509.21393","title":"Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2301.00106","title":"Physics-informed Neural Networks approach to solve the Blasius function","created":"2022-12-31","cited_by_count":1,"area":null},{"id":"2309.04755","title":"A Novel Training Framework for Physics-informed Neural Networks: Towards Real-time Applications in Ultrafast Ultrasound Blood Flow Imaging","created":"2023-09-09","cited_by_count":1,"area":null},{"id":"2402.07153","title":"Error Estimation for Physics-informed Neural Networks Approximating Semilinear Wave Equations","created":"2024-02-11","cited_by_count":1,"area":null},{"id":"2402.16281","title":"RobKiNet: Robotic Kinematics Informed Neural Network for Optimal Robot Configuration Prediction","created":"2024-02-26","cited_by_count":1,"area":null},{"id":"2404.01163","title":"Capturing Shock Waves by Relaxation Neural Networks","created":"2024-04-01","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":"2409.04708","title":"Harnessing physics-informed operators for high-dimensional reliability analysis problems","created":"2024-09-07","cited_by_count":1,"area":null},{"id":"2502.08783","title":"Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2503.16777","title":"Physics-Informed Deep B-Spline Networks","created":"2025-03-21","cited_by_count":1,"area":null},{"id":"2503.23729","title":"Integral regularization PINNs for evolution equations","created":"2025-03-31","cited_by_count":1,"area":null},{"id":"2602.07364","title":"FEM-Informed Hypergraph Neural Networks for Efficient Elastoplasticity","created":"2026-02-07","cited_by_count":1,"area":null},{"id":"2607.18020","title":"FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers","created":"2026-07-20","cited_by_count":1,"area":null},{"id":"2608.07053","title":"Unsupervised Adaptation of PDE Foundation Models","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2211.13464","title":"Design of Turing Systems with Physics-Informed Neural Networks","created":"2022-11-24","cited_by_count":0,"area":null},{"id":"2302.08309","title":"The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach","created":"2023-02-16","cited_by_count":0,"area":null},{"id":"2310.04789","title":"HNS: An Efficient Hermite Neural Solver for Solving Time-Fractional Partial Differential Equations","created":"2023-10-07","cited_by_count":0,"area":null},{"id":"2312.05891","title":"A conservative hybrid physics-informed neural network method for Maxwell-Amp\\`{e}re-Nernst-Planck equations","created":"2023-12-10","cited_by_count":0,"area":null},{"id":"2409.03239","title":"DiffGrad for Physics-Informed Neural Networks","created":"2024-09-05","cited_by_count":0,"area":null},{"id":"2409.14722","title":"Neural refractive index field: Unlocking the Potential of Background-oriented Schlieren Tomography in Volumetric Flow Visualization","created":"2024-09-23","cited_by_count":0,"area":null},{"id":"2504.03469","title":"Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data","created":"2025-04-04","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":"2505.11682","title":"Mollifier Layers: Enabling Efficient High-Order Derivatives in Inverse PDE Learning","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2509.25450","title":"Multi-patch isogeometric neural solver for partial differential equations on computer-aided design domains","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2511.01911","title":"Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problems","created":"2025-10-31","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":"2603.24002","title":"Stochastic Dimension Zeroth-Order Estimator: Stable and Memory-Efficient Training of PINNs","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2604.09361","title":"Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2604.14472","title":"Auxiliary Finite-Difference Residual-Gradient Regularization for PINNs","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2605.29688","title":"A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2607.13566","title":"Spectral-Informed Neural Networks Outperform Spectral Methods in High-dimensional PDEs","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.11020","title":"Derivative Computation in PINNs: Automatic Differentiation, Finite Differences and Beyond","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.16080","title":"DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.29521","title":"Online Gate-Driven Flow Control in Resin Transfer Moulding Using a Neural-Network Surrogate","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.02110","title":"A Computational Comparison of Fourier Spectral Differentiation and Spatial Automatic Differentiation in Periodic Physics-Informed Neural Networks","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2609.33078","title":"The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}