We present a novel computational framework that can compute global solutions to high-
dimensional dynamic stochastic economic models on irregular state space geometries. This …

We propose a novel structural estimation framework in which we train a surrogate of an
economic model with deep neural networks. Our methodology alleviates the curse of …

Solving dynamic economic models that capture salient real-world heterogeneity and non-
linearity requires the approximation of high-dimensional functions. As their dimensionality …

New generation airborne embedded system has deployed Graphical Processing Units
(GPUs) to raise processing capability to meet growing computational demands. Comparing …

We propose a scalable method for computing global solutions of nonlinear, high-
dimensional dynamic stochastic economic models. First, within a time iteration framework …

This paper introduces the concept of``self-justified equilibria" as a tractable alternative to
rational expectations equilibria in stochastic general equilibrium models with heterogeneous …

We introduce a novel numerical framework for pricing American options in high dimensions.
Our scheme manages to alleviate the problem of dimension scaling through the use of …

Today, one of the main challenges for high-performance computing systems is to improve
their performance by keeping energy consumption at acceptable levels. In this context, a …

Abstract We introduce``deep surrogates''--high-precision approximations of structural
models based on deep neural networks, which speed up model evaluation and estimation …

Fundamental tasks in multivariate and numerical analysis, such as sparse precision matrix
estimation via graphical lasso and function approximation, are formulated in ever-increasing …