{"total":72,"rows":[{"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":"2408.11104","title":"ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks","created":"2024-08-20","cited_by_count":46,"area":null},{"id":"2405.14369","title":"RoPINN: Region Optimized Physics-Informed Neural Networks","created":"2024-05-23","cited_by_count":26,"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":"2409.18426","title":"Dual Cone Gradient Descent for Training Physics-Informed Neural Networks","created":"2024-09-27","cited_by_count":21,"area":null},{"id":"2407.15887","title":"Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning","created":"2024-07-21","cited_by_count":15,"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":"2505.10949","title":"FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural Networks","created":"2025-05-16","cited_by_count":12,"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":"2312.05583","title":"Better Neural PDE Solvers Through Data-Free Mesh Movers","created":"2023-12-09","cited_by_count":9,"area":null},{"id":"2502.04917","title":"Complex Physics-Informed Neural Network","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2412.09009","title":"A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems","created":"2024-12-12","cited_by_count":6,"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":"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":"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":"2402.11283","title":"Deep adaptive sampling for surrogate modeling without labeled data","created":"2024-02-17","cited_by_count":5,"area":null},{"id":"2410.18153","title":"Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees","created":"2024-10-23","cited_by_count":5,"area":null},{"id":"2505.11117","title":"Dual-Balancing for Physics-Informed Neural Networks","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2405.08558","title":"PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs","created":"2024-05-14","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":"2504.05248","title":"PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks","created":"2025-04-07","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":"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":"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":"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":"2512.01370","title":"PRISMA: Improving the Accuracy-Latency Frontier of Diffusion-based PDE Solvers Using Physics-Informed Spectral Attention","created":"2025-12-01","cited_by_count":3,"area":null},{"id":"2310.05169","title":"Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp","created":"2023-10-08","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":"2409.20206","title":"SetPINNs: Set-based Physics-informed Neural Networks","created":"2024-09-30","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":"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":"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":"2404.01163","title":"Capturing Shock Waves by Relaxation Neural Networks","created":"2024-04-01","cited_by_count":1,"area":null},{"id":"2409.08958","title":"PINNfluence: Interpreting PINNs through Influence Functions","created":"2024-09-13","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":"2512.15083","title":"Neural Modular Physics for Elastic Simulation","created":"2025-12-17","cited_by_count":1,"area":null},{"id":"2603.05495","title":"Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels","created":"2026-03-05","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":"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":"2301.07361","title":"Dirichlet-Neumann learning algorithm for solving elliptic interface problems","created":"2023-01-18","cited_by_count":0,"area":null},{"id":"2403.04883","title":"Learning Traveling Solitary Waves Using Separable Gaussian Neural Networks","created":"2024-03-07","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":"2408.17246","title":"(Un)supervised Learning of Maximal Lyapunov Functions","created":"2024-08-30","cited_by_count":0,"area":null},{"id":"2504.00910","title":"Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks","created":"2025-04-01","cited_by_count":0,"area":null},{"id":"2504.18091","title":"Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning","created":"2025-04-25","cited_by_count":0,"area":null},{"id":"2506.20441","title":"M\\'ethode de quadrature pour les PINNs fond\\'ee th\\'eoriquement sur la hessienne des r\\'esiduels","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2507.01714","title":"B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2509.11768","title":"Stabilizing PINNs: A regularization scheme for PINN training to avoid unstable fixed points of dynamical systems","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2510.23999","title":"Auto-Adaptive PINNs with Applications to Phase Transitions","created":"2025-10-28","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":"2511.12512","title":"xLSTM-PINN: Memory-Gated Spectral Remodeling for Physics-Informed Learning","created":"2025-11-16","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},{"id":"2603.15526","title":"Building Trust in PINNs: Error Estimation through Finite Difference Methods","created":"2026-03-16","cited_by_count":0,"area":null},{"id":"2603.22700","title":"Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2605.15254","title":"Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.19263","title":"From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2605.30910","title":"PINNs Failure Modes are Overfitting","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.02623","title":"Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.04736","title":"Curvature-aware dynamic precision approach for physics-informed neural networks","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.09434","title":"A transition-density-based operator learning method for Fokker-Planck equations with various initial conditions","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2607.07623","title":"Higher-Order Geometric Updates for Levenberg-Marquardt Method via Riemann Normal Coordinates","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.11094","title":"Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.14233","title":"LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2607.15751","title":"Trainable Spline Representations for Physics-Informed Learning","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2609.20641","title":"Beyond PINNs: A Unified Gauss--Newton and Petrov--Galerkin Framework for Neural and Hybrid PDE Solvers","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2609.30966","title":"Gradient Surgery for Physics-Informed Neural Networks","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.36615","title":"CI-PINN: Causal Integral Physics-Informed Neural Network for Solving Evolution Equations","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}