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Search · four archives
13 papers · ranked by Valyu relevance
B. K. M. Case, Jean-Gabriel Young, Daniel Penados, Carlota Monroy + 3 more
Widespread application of insecticide remains the primary form of control for Chagas disease in Central America, despite only temporarily reducing domestic levels of the endemic vector Triatoma dimidiata and having little long-term impact. Recently, an approach emphasizing community feedback and housing improvements…
Yanbing Liu, Liping Chen, Yu Chen, Jianwan Ding + 2 more
'Franz Martin Rohrhofer' 'Bernhard C. Geiger'] Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in…
Suryateja Ravutla, Andrew Bai, Matthew J. Realff, Fani Boukouvala
Hybridization and Adaptive Sampling for Simulation-Based Optimization Authors: ['Suryateja Ravutla' 'Andrew Bai' 'Matthew J. Realff' 'Fani Boukouvala'] Process simulators are essential for modeling of complex processes; however, optimization of expensive models remains challenging due to lack of equations, simulation…
Félix Mercier, Nizar Bouhlel, Angelina El Ghaziri, Joseph Ly Vu + 2 more
Digital phenotyping is rapidly advancing, generating increasing amounts of data, particularly in the case of temporal monitoring. We propose an adaptive sampling method that optimizes sampling, thereby reducing costs associated with data production, processing, and storage. The proposed method is based on Bayesian…
Mateu Sbert, László Szirmay-Kalos, Chun-Hung Liu, Jwo-Yuh Wu + 1 more
'Peter Y. Hong'] Multiple Importance Sampling (MIS) combines the probability density functions (pdf) of several sampling techniques. The combination weights depend on the proportion of samples used for the particular techniques. Weights can be found by optimization of the variance, but this approach is costly and…
Jiaming Liang, Zhanchao Li, Litan Pan, Ebrahim Yahya Khailah + 2 more
'Linsong Sun' 'Weigang Lu'] Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to…
Tai-Sung Lee, Omid Jahanmahin, Saikat Pal, Darrin M. York
Adaptive Thermodynamic Integration for Alchemical Free Energy Calculations Authors: Tai-Sung Lee, Omid Jahanmahin, Saikat Pal, Darrin M. York Accurate and efficient calculation of alchemical free energies is a critical challenge in computational chemistry, frequently hindered by the inherent limitations of conventional…
Yan Liu, Maojun Zhang, Zhiwei Zhong, Xiangrong Zeng
In this work, we introduce AdaCN, a novel adaptive cubic Newton method for nonconvex stochastic optimization. AdaCN dynamically captures the curvature of the loss landscape by diagonally approximated Hessian plus the norm of difference between previous two estimates. It only requires at most first order gradients and…
Fulvia Mecatti, Charalambos Sismanidis, Emanuela Furfaro, Pier Luigi Conti
'Pier Luigi Conti'] A new class of sampling strategies is proposed that can be applied to population-based surveys targeting a rare trait that is unevenly spread over an area of interest. Our proposal is characterised by the ability to tailor the data collection to specific features and challenges of the survey at…
Javier Garcia-Barcos, Ruben Martinez-Cantin, Eduardo C. Garrido-Merchán
'Eduardo C. Garrido-Merchán'] Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of…
Sabina J. Sloman, Daniel R. Cavagnaro, Stephen B. Broomell
Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro et al., [9]). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO…
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization methods for BBVI remain unreliable and require substantial expertise and…
Maliki Moustapha, Alina Galimshina, Guillaume Habert, Bruno Sudret
Explicitly accounting for uncertainties is paramount to the safety of engineering structures. Optimization which is often carried out at the early stage of the structural design offers an ideal framework for this task. When the uncertainties are mainly affecting the objective function, robust design optimization is…