Author

Monica Sigovan

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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

Open access Aug 2026

Meta-SPINN: meta-learned basis adaptation for parametric PDEs

Meta-SPINN works both as a direct predictor for unseen tasks and as a task-aware initializer for subsequent single-instance residual-guided refinement, providing reusable predictions together with an interpretable visualization of how solution geometry changes across a parameter family.

Vikas Dwivedi, Monica Sigovan, Bruno Sixou · Machine Learning: Science an... · 0 citations