Joshua L. Padgett
Orcid: 0000-0001-9369-351X
According to our database1,
Joshua L. Padgett
authored at least 10 papers
between 2017 and 2024.
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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
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