most citedDensity Propagation with Characteristics-based Deep Learning

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

physics.comp-ph2020

Improving neural network predictions of material properties with limited data using transfer learning

Schuyler Krawczuk, Daniele Venturi

We develop new transfer learning algorithms to accelerate prediction of material properties from ab initio simulations based on density functional theory (DFT). Transfer learning h…

math.DS20191 cited

Density Propagation with Characteristics-based Deep Learning

Tenavi Nakamura-Zimmerer, Daniele Venturi, Qi Gong +1

Uncertainty propagation in nonlinear dynamic systems remains an outstanding problem in scientific computing and control. Numerous approaches have been developed, but are limited in…

math.NA2019

A new scalable algorithm for computational optimal control under uncertainty

Panos Lambrianides, Qi Gong, Daniele Venturi

We address the design and synthesis of optimal control strategies for high-dimensional stochastic dynamical systems. Such systems may be deterministic nonlinear systems evolving fr…

math.NA2019

Stability analysis of hierarchical tensor methods for time-dependent PDEs

Abram Rodgers, Daniele Venturi

In this paper we address the question of whether it is possible to integrate time-dependent high-dimensional PDEs with hierarchical tensor methods and explicit time stepping scheme…

math.NA2019

Dynamically orthogonal tensor methods for high-dimensional nonlinear PDEs

Alec Dektor, Daniele Venturi

We develop new dynamically orthogonal tensor methods to approximate multivariate functions and the solution of high-dimensional time-dependent nonlinear partial differential equati…

math.NA2019

Generalized Langevin equations for systems with local interactions

Yuanran Zhu, Daniele Venturi

We present a new method to approximate the Mori-Zwanzig (MZ) memory integral in generalized Langevin equations (GLEs) describing the evolution of smooth observables in high-dimensi…