We propose machine learning methods for solving fully nonlinear partial differential equations (PDEs) with convex Hamiltonian. Our algorithms are conducted in two steps. First, the PDE is rewritten its dual stochastic control representation form, and corresponding optimal feedback estimated using a neural network. Next, three different presented to approximate associated value function, i.e., s...

We consider a Poisson equation in Rd for the elliptic operator corresponding to an ergodic diffusion process. Optimal regularity and smoothness with respect parameter are obtained under mild conditions on coefficients. The result is then applied establish general approximation fully coupled multitime scales stochastic differential equations only Hölder continuous Four different averaged as well...

Inverter-based Operational Transconductance Amplifiers (OTAs) are versatile and friendly scalable analog circuit blocks. Especially for the new CMOS technological nodes, several recent applications have been extensively using them, ranging from Analog Front End (AFE) to analog-to-digital converters (ADC). This work tracks down current advances in inverter-based OTAs design, comparing their basi...

This paper presents a new CMOS fully differential second-generation current conveyor (FDCCII). The proposed FDCCII is based on a fully differential difference transconductor as an input stage and two class AB output stages. Besides the proposed FDCCII circuit is operating at supply voltages of ± 1.5 V, it has a total standby current of 380 μA. The application of the FDCCII to realize variable g...