118 citations · 129 across the 12 of their papers we have counts for
3 papers · 2 filters
Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
Vikas Dwivedi, Enrico Schiassi, Monica Sigovan +1
Physics-informed neural networks (PINNs) and related methods struggle to resolve sharp gradients in singularly perturbed boundary value problems without resorting to some form of d…
Kernel-Adaptive PI-ELMs for Forward and Inverse Problems in PDEs with Sharp Gradients
Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan +1
Physics-informed machine learning frameworks such as Physics-Informed Neural Networks (PINNs) and Physics-Informed Extreme Learning Machines (PI-ELMs) have shown great promise for…
Curriculum Learning-Driven PIELMs for Fluid Flow Simulations
Vikas Dwivedi, Bruno Sixou, Monica Sigovan
This paper presents two novel, physics-informed extreme learning machine (PIELM)-based algorithms for solving steady and unsteady nonlinear partial differential equations (PDEs) re…