Surrogate and reduced-order modeling: a comparison of approaches for large-scale statistical inverse problems [Chapter 7]

Solution of statistical inverse problems via the frequentist or Bayesian approaches described in earlier chapters can be a computationally intensive endeavor, particularly when faced with large-scale forward models characteristic of many engineering and science applications. High computational cost arises in several ways. First, thousands or millions of forward simulations may be required to evaluate estimators of interest or to characterize a posterior distribution. In the large-scale setting, performing so many forward simulations is often computationally intractable. Second, sampling may be complicated by the large dimensionality of the input space--as when the inputs are fields represented with spatial discretizations of high dimension--and by nonlinear forward dynamics that lead to multimodal, skewed, and/or strongly correlated posteriors. In this chapter, we present an overview of surrogate and reduced order modeling methods that address these computational challenges. For illustration, we consider a Bayesian formulation of the inverse problem. Though some of the methods we review exploit prior information, they largely focus on simplifying or accelerating evaluations of a stochastic model for the data, and thus are also applicable in a frequentist context.Sandia National Laboratories (Laboratory Directed Research and Development (LDRD) program)United States. Dept. of Energy (Contract DE-AC04-94AL85000)Singapore-MIT Alliance Computational Engineering ProgrammeUnited States. Dept. of Energy (Award Number DE-FG02-08ER25858 )United States. Dept. of Energy (Award Number DESC00025217

Adaptive rational interpolation: Arnoldi and Lanczos-like equations

Accepted versio

A graph-based approach to client relationship management in fund administration

Ensuring effective and timely communication in the fund administration industry has become an essential element of client relationship management (CRM). A CRM team periodically evaluates asset managers perception of service quality using surveys, where it can be difficult to evaluate service quality objectively. Within the asset management industry, despite ongoing technological advances, the main channel of communication remains email. The sheer volume of email, and the industry’s reliance on it, can lead to practical problems that impact CRM. In this work we draw insights from the email communications between an operations team and two-sample clients to understand client relationships in a way not previously possible. The results are presented and we discuss how these can quantitatively support and improve service quality evaluations in CRM. For this application, we exploit the social relations in emails via a graph-based approach. A deep learning framework is described that allows a graph-based inspection of the email communications between asset managers and their fund administrators operations teams. The presented framework integrates a natural language processing model to transform email subject lines to embedding representations, a knowledge graph to transform the email communication links into a graph representation, and a graph neural network to process the embedding representations and classify the email communications. The classification of critical conversations via email is a demonstrative example of a scalable graph-based approach that allows the use of machine learning to process, learn, and explore the relations existing between the emails