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ORCIDJoshua L. Padgett
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Journal Articles
- 2021
[j4]Joshua Lee Padgett
, Yusup Geldiyev, Sakshi Gautam, Wenjing Peng, Yehia Mechref, Akif I. Ibraguimov:
Object classification in analytical chemistry via data-driven discovery of partial differential equations. Comput. Math. Methods 3(4) (2021)- 2018
- [j3]Joshua L. Padgett, Qin Sheng:
Numerical solution of degenerate stochastic Kawarada equations via a semi-discretized approach. Appl. Math. Comput. 325: 210-226 (2018) - [j2]Joshua L. Padgett:
The quenching of solutions to time-space fractional Kawarada problems. Comput. Math. Appl. 76(7): 1583-1592 (2018) - 2017
- [j1]Matthew A. Beauregard, Joshua L. Padgett, Rana D. Parshad:
A nonlinear splitting algorithm for systems of partial differential equations with self-diffusion. J. Comput. Appl. Math. 321: 8-25 (2017)
Informal and Other Publications
- 2024
- [i6]Shakil Rafi, Joshua Lee Padgett, Ukash Nakarmi:
Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials. CoRR abs/2402.01058 (2024) - [i5]Julia Ackermann, Arnulf Jentzen, Benno Kuckuck, Joshua Lee Padgett:
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 abs/2406.10876 (2024) - 2023
- [i4]Julia Ackermann, Arnulf Jentzen, Thomas Kruse, Benno Kuckuck, Joshua Lee Padgett:
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 Lp-sense. CoRR abs/2309.13722 (2023) - 2021
- [i3]Tiffany Frugé Jones, Evdokiya Georgieva Kostadinova, Joshua Lee Padgett, Qin Sheng:
A series representation of the discrete fractional Laplace operator of arbitrary order. CoRR abs/2101.03629 (2021) - [i2]Joshua Lee Padgett, Eduardo Servin:
A positivity- and monotonicity-preserving nonlinear operator splitting approach for approximating solutions to quenching-combustion semilinear partial differential equations. CoRR abs/2109.05345 (2021) - [i1]Martin Hutzenthaler, Arnulf Jentzen, Benno Kuckuck, Joshua Lee Padgett:
Strong Lp-error analysis of nonlinear Monte Carlo approximations for high-dimensional semilinear partial differential equations. CoRR abs/2110.08297 (2021)
Coauthor Index
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