Joshua L. Padgett

According to our database, Joshua L. Padgett authored at least 9 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials.

Shakil Rafi, Joshua Lee Padgett, Ukash Nakarmi

CoRR, 2024

2023

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the L^p-sense.

Julia Ackermann, Arnulf Jentzen, Thomas Kruse, Benno Kuckuck, Joshua Lee Padgett

CoRR, 2023

2021

Strong L^p-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations.

Martin Hutzenthaler, Arnulf Jentzen, Benno Kuckuck, Joshua Lee Padgett

CoRR, 2021

A positivity- and monotonicity-preserving nonlinear operator splitting approach for approximating solutions to quenching-combustion semilinear partial differential equations.

Joshua Lee Padgett, Eduardo Servin

CoRR, 2021

A series representation of the discrete fractional Laplace operator of arbitrary order.

Tiffany Frugé Jones, Evdokiya Georgieva Kostadinova, Joshua Lee Padgett, Qin Sheng

CoRR, 2021

Object classification in analytical chemistry via data-driven discovery of partial differential equations.

Joshua Lee Padgett, Yusup Geldiyev, Sakshi Gautam, Wenjing Peng, Yehia Mechref, Akif I. Ibraguimov

Comput. Math. Methods, 2021

2018

The quenching of solutions to time-space fractional Kawarada problems.

Joshua L. Padgett

Comput. Math. Appl., 2018

Numerical solution of degenerate stochastic Kawarada equations via a semi-discretized approach.

Joshua L. Padgett, Qin Sheng

Appl. Math. Comput., 2018

2017

A nonlinear splitting algorithm for systems of partial differential equations with self-diffusion.

Matthew A. Beauregard, Joshua L. Padgett, Rana D. Parshad

J. Comput. Appl. Math., 2017