A multi-network PINN with NTK-based adaptive weighting jointly estimates source functions, velocity, diffusion parameters, and the solution field in advection-diffusion PDEs from noisy sparse data.
Physics-informed learning of governing equations from scarce data.Nature communications, 12(1):6136
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FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
EqOD combines symmetry-based library reduction with stability selection to reach F1=1.000 on several noisy PDE identification tasks where prior methods fail.
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Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
A multi-network PINN with NTK-based adaptive weighting jointly estimates source functions, velocity, diffusion parameters, and the solution field in advection-diffusion PDEs from noisy sparse data.
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FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
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EqOD: Symmetry-Informed Stability Selection for PDE Identification
EqOD combines symmetry-based library reduction with stability selection to reach F1=1.000 on several noisy PDE identification tasks where prior methods fail.