Author

Balaji Srinivasan

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Preprint Oct 2026

AB-PIELMs: Adaptive-Basis Physics-Informed Extreme Learning Machines for Residual-Driven Domain Decomposition

Physics-informed neural solvers often struggle with multiscale behavior and sharp gradients due to the use of global approximation spaces and nonconvex training. We introduce AB-PIELM, an adaptive-basis physics-informed extreme learning machine that combines domain decomposition with joint optimisation of spatial and s...

A. Daniel, Vikas Dwivedi, Balaji Srinivasan · 1 citation

Preprint Oct 2026

CFKS: Continuum-Feature Kernel PDE Solvers

Neural-network-based PDE solvers require architectural choices, particularly the number of layers and neurons per layer. Fixing a single Gaussian radial basis function (RBF) feature layer and taking the number of features to the continuum limit removes the need to prescribe a finite feature count or select individual f...

Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan et al. · 0 citations

Oct 2026

KAPI-LM: kernel-adaptive physics-informed learning machines for PDEs.

Physics-informed neural networks (PINNs) have emerged as mesh-free approaches for solving partial differential equations, but their learned representations are often difficult to interpret. RBF-kernel-based formulations such as sparse, physics-based, and interpretable neural networks (SPINNs) make the geometry of the p...

Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan et al. · Neural Networks · 0 citations