Joshua L. Padgett

Orcid: 0000-0001-9369-351X

According to our database1, Joshua L. Padgett authored at least 10 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations.
CoRR, 2024

Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials.
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<sup>p</sup>-sense.
CoRR, 2023

2021
Strong L<sup>p</sup>-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations.
CoRR, 2021

A positivity- and monotonicity-preserving nonlinear operator splitting approach for approximating solutions to quenching-combustion semilinear partial differential equations.
CoRR, 2021

A series representation of the discrete fractional Laplace operator of arbitrary order.
CoRR, 2021

Object classification in analytical chemistry via data-driven discovery of partial differential equations.
Comput. Math. Methods, 2021

2018
The quenching of solutions to time-space fractional Kawarada problems.
Comput. Math. Appl., 2018

Numerical solution of degenerate stochastic Kawarada equations via a semi-discretized approach.
Appl. Math. Comput., 2018

2017
A nonlinear splitting algorithm for systems of partial differential equations with self-diffusion.
J. Comput. Appl. Math., 2017


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