6 papers
Data-informed lifting line theory
Arjun Sharma, Jonas A. Actor, Peter A. Bosler
We present a data-driven framework that extends the predictive capability of classical lifting-line theory (LLT) to a wider aerodynamic regime by incorporating higher-fidelity aero…
Multilevel Training for Kolmogorov Arnold Networks
Ben S. Southworth, Jonas A. Actor, Graham Harper +1
Algorithmic speedup of training common neural architectures is made difficult by the lack of structure guaranteed by the function compositions inherent to such networks. In contras…
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs
Adrienne M. Propp, Jonas A. Actor, Elise Walker +3
Dirichlet-to-Neumann maps enable the coupling of multiphysics simulations across computational subdomains by ensuring continuity of state variables and fluxes at artificial interfa…
Deriving Transformer Architectures as Implicit Multinomial Regression
Jonas A. Actor, Anthony Gruber, Eric C. Cyr
While attention has been empirically shown to improve model performance, it lacks a rigorous mathematical justification. This short paper establishes a novel connection between att…
Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement
Jonas A. Actor, Graham Harper, Ben Southworth +1
Multilayer perceptrons (MLPs) are a workhorse machine learning architecture, used in a variety of modern deep learning frameworks. However, recently Kolmogorov-Arnold Networks (KAN…
Mixture of neural operator experts for learning boundary conditions and model selection
Dwyer Deighan, Jonas A. Actor, Ravi G. Patel
While Fourier-based neural operators are best suited to learning mappings between functions on periodic domains, several works have introduced techniques for incorporating non triv…