Physics-Informed Neural Networks Comparison Hub
5 papers - avg viability 5.0
Top Papers
- UniPINN: A Unified PINN Framework for Multi-task Learning of Diverse Navier-Stokes Equations(7.0)
UniPINN is a unified framework for multi-task learning of diverse Navier-Stokes equations using Physics-Informed Neural Networks.
- Physics-informed neural operator for predictive parametric phase-field modelling(6.0)
A physics-informed neural operator framework that accelerates parametric phase-field modeling for materials science.
- Building Trust in PINNs: Error Estimation through Finite Difference Methods(6.0)
A method for estimating errors in physics-informed neural networks to enhance trust and interpretability in their predictions.
- Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask(4.0)
A hybrid neural operator for efficient simulation of EUV electromagnetic wave diffraction from lithography masks.
- Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity(2.0)
This paper explores the scaling laws and optimization challenges of Single-Layer Physics-Informed Neural Networks in solving nonlinear PDEs.