We introduce a novel approach to waveform inversion based on data-driven reduced order model (ROM) of the wave operator. The presentation is for acoustic equation, but can be extended elastic or electromagnetic waves. data are time resolved measurements pressure gathered by an acquisition system that probes unknown medium with pulses and measures generated propose solve inverse problem velocity...

In this work we propose a novel approach to model order reduction for incompressible fluid flows that focuses on the spatio-temporal description of the stresses on the surface of a body, i.e. of the wall shear stress and of the wall pressure. The spatial representation of these two variables is given by a compact set of “wall basis functions”, i.e. elementary basis functions defined on the wall...

This talk proposes an approximate deconvolution reduced order model (AD-ROM). The new AD-ROM is developed within the large eddy simulation reduced order modeling (LES-ROM) framework, in which a differential filter is used to define the large ROM structures and the AD approach is used to solve the ROM closure problem. The AD-ROM is tested in the numerical simulation of a three-dimensional flow p...

As the size of the integrated circuits become smaller with higher speed of operation, the RLC interconnect effects have greater impact on signal propagation and signal integrity issue that is reflected in terms of noise or cross talk effects and giving rise to delay, ringing, glitches and overshoot, undershoot or false signaling problems etc. (figure-1, [7]). These definitely impose a great cha...

A reduced order modeling method based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition technique is presented. The link with Padé approximation, the block Arnoldi process and singular value decomposition (SVD) leads to a simple and stable implementation of the algorithm. Novel features of the approach include the determination of ...

In this paper, we discuss the problem of minimizing the sum of two convex functions: a smooth function plus a non-smooth function. Further, the smooth part can be expressed by the average of a large number of smooth component functions, and the non-smooth part is equipped with a simple proximal mapping. We propose a proximal stochastic second-order method, which is efficient and scalable. It in...

Purpose: We use reduced-order constrained optimization (ROCO) to create clinically acceptable IMRT plans quickly and automatically for advanced lung cancer patients. Our new ROCO implementation works with the treatment planning system and full dose calculation used at Memorial Sloan-Kettering Cancer Center, and we have implemented mean dose hard-constraints, along with the point-dose and dose-v...

In distributed control system Ethernet is invading communication areas reserved so far to other specific and/or proprietary networks, due to its wide bandwidth, reliability and reduced cost. However this fact obliges to carefully evaluate the delay that messages undergo in order to fulfill with the application temporal requirements. In this paper the delay model is studied, its main components ...