Abstract: In this study, to obtain Born approximation multi-resolution imaging for transient electromagnetic (TEM) pseudo wavefield, employing a series of differential pulse with multiple pulse widths ...

Solver for coupled nonlinear partial differential equations (elliptic-parabolic conservation laws) based on the Voronoi finite volume method. It uses automatic differentiation via ForwardDiff.jl and ...

Adequate mathematical modeling is the key to success for many real-world projects in engineering, medicine, and other applied areas. Once a well-suited model is established, it can be thoroughly ...

This paper proposes a new deep-learning-based algorithm for high-dimensional Bermudan option pricing. To the best of our knowledge, this is the first study of the arbitrary-order discretization scheme ...

Simo Särkkä and Arno Solin (2019). Applied Stochastic Differential Equations. Cambridge University Press. Cambridge, UK. The book can be ordered through Cambridge University Press or, e.g., from ...

This course focuses on three-dimensional geometry processing, while simultaneously providing a first course in traditional differential geometry. Our main goal is to show how fundamental geometric ...

In this work, we establish a fractional-order neural field mathematical model with Caputo's fractional derivative temporal order α considering 0 < α < 2, to analyze the effect of fractional-order on ...

This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial ...

In many physical systems, the governing equations are known with high confidence, but direct numerical solution is prohibitively expensive. Often this situation is alleviated by writing effective ...

Abstract: This paper designs a close loop Σ-Δ readout circuit for differential MEMS accelerometer. A technique named oversampling successive approximation (OSA) is ...