1 citations · 1 across the 3 of their papers we have counts for
3 papers
SUPN: Shallow Universal Polynomial Networks for Efficient Function Approximation
Zachary Morrow, Michael Penwarden, Brian Chen +3
Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are flexible function approximators, but their accuracy can require substantial network depth, width, and trainabl…
Verification and Validation for Trustworthy Scientific Machine Learning
John D. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins +1
Scientific machine learning (SciML) models are transforming many scientific disciplines. However, the development of good modeling practices to increase the trustworthiness of SciM…
Democratizing Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard +22
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In thi…