2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
Bruno Jacob, Amanda A. Howard, Panos Stinis
Quantum algorithms for partial differential equations (PDEs) face severe practical constraints on near-term hardware: limited qubit counts restrict spatial resolution to coarse gri…
cs.LG2025
E-PINNs: Epistemic Physics-Informed Neural Networks
Bruno Jacob, Ashish S. Nair, Amanda A. Howard +2
Physics-informed neural networks (PINNs) have demonstrated promise as a framework for solving forward and inverse problems involving partial differential equations. Despite recent…
cs.LG2024★ 2 cited
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks
Bruno Jacob, Amanda A. Howard, Panos Stinis
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use mul…