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Department of Mathematics and Computer Sciences, Marist College, Poughkeepsie, NY, USA. This equation arises in many fields of mathematics and physics. It has applications in random processes, optimal ...
Nonlinear differential equations appear in many domains and are notoriously difficult to solve. Whereas previous quantum algorithms for general nonlinear differential equations have complexity ...
A computational revolution unleashed the power of artificial neural networks. At the heart of that revolution is automatic differentiation, which calculates the derivative of a performance measure ...
EVEN from the point of view of an undergraduate, the subject of differential equations is very diiferent from what it was fifty years ago. But in a large and miscellaneous collection of examples like ...
SUNDIALS is a family of software packages providing robust and efficient time integrators and nonlinear solvers that can easily be incorporated into existing simulation codes. The packages are ...
Abstract: Finite difference methods are well‐known numerical methods to solve differential equations by approximating the derivatives using different difference schemes. Theoretical results have been ...
ABSTRACT: A transformation way of the Navier-Stokes differential equation was presented. The obtained result is the Cauchy momentum equation. The transformation was performed using a novel shorten ...
Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), ...
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