2020
Kushal Chakrabarti, Nirupam Gupta, Nikhil Chopra
The authors propose an iterative pre-conditioning method that significantly reduces the impact of the conditioning of the minimization problem on the convergence rate of the traditional gradient-descent algorithm.
arXiv.org
Tianchen Liu, Shapour Azarm, Nikhil Chopra
Two decentralized (multi-level and bi-level) approaches are formulated to solve multi-subsystem co-design problems, which are based on the direct collocation and decomposition-based optimization methods.
Journal of Mechanical Design
2019
Thurston Sexton, Mark Fuge
The paper directly models a cognitive process by which technicians may record work orders, recovering implied engineering knowledge about system structure by processing written records.
ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference
Keven Chiu, Mark Fuge
From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions.
ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference
2020
Jeffrey W. Herrmann and multiple colleagues
From 2005–2013, Professor Jeffrey W. Herrmann (ME/ISR) worked on public health research with the U.S. Centers for Disease Control and Prevention (CDC); the Montgomery County, Maryland, Advanced Practice Center for Public Health Emergency Preparedness and Response; and the National Association of County and City Health Officials (NACCHO).
Specifically, Herrmann and his colleagues created mathematical and simulation models of mass dispensing and vaccination clinics (also known as points of dispensing or PODs). They also developed decision support tools to help emergency preparedness planners design clinics that have enough capacity to serve residents quickly while avoiding unnecessary congestion.
A poor clinic design has insufficient capacity and long lines of patients waiting for vaccinations or other services such as testing and triage. More patients require more space as they wait to receive treatment. If too many patients are in the clinic, they cause congestion, crowding, confusion, and the potential spread of disease. Herrmann's models and support tools help planners design clinics that have better patient flow and are able to process people more quickly and efficiently.
This research originally was developed with mass dispensing and vaccination clinics in mind. However, the principles behind the models and support tools are useful for those setting up emergency clinics to combat the COVID-19 pandemic. They are offered free of charge.
2019
Dilek Günneç, S. Raghavan, Rui Zhang
This paper focuses on the NP-hard “least-cost influence problem (LCIP)”: an influence-maximization problem where the goal is to find the least-expensive way of maximizing influence over a social network.
INFORMS Journal on Computing, Nov. 26, 2019
2020
Ye Chen, Ilya Ryzhov
Introduces a new consistency theory that interprets approximate Bayesian inference as a form of stochastic approximation (SA) with an additional “bias” term. Derives the first consistency proofs for a suite of approximate Bayesian models.
Operations Research
2019
Daniel Lemkin, Benoit Stryckman, Joel Klein, Jason Custer, William Bame, Louis Maranda, Kenneth Wood, Courtney Paulson, Zachary Dezman
Integrating a safety smart list into the electronic health record decreases intensive care unit length of stay and cost.
Journal of Critical Care
