Zobrazeno 1 - 10
of 500
pro vyhledávání: '"Higdon, David"'
Autor:
Fadikar, Arindam, Stevens, Abby, Collier, Nicholson, Toh, Kok Ben, Morozova, Olga, Hotton, Anna, Clark, Jared, Higdon, David, Ozik, Jonathan
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, particularly in real-time or online environments where data arrives sequen
Externí odkaz:
http://arxiv.org/abs/2402.15619
Autor:
Moran, Kelly R., Heitmann, Katrin, Lawrence, Earl, Habib, Salman, Bingham, Derek, Upadhye, Amol, Kwan, Juliana, Higdon, David, Payne, Richard
Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness; this trend is expected to continue into the future as new ground- and space-based surveys come online. In order to maximally extr
Externí odkaz:
http://arxiv.org/abs/2207.12345
Publikováno v:
In Journal of Manufacturing Systems February 2024 72:1-15
Deep Gaussian processes (DGPs) are increasingly popular as predictive models in machine learning (ML) for their non-stationary flexibility and ability to cope with abrupt regime changes in training data. Here we explore DGPs as surrogates for compute
Externí odkaz:
http://arxiv.org/abs/2012.08015
Autor:
Baker, Evan, Barbillon, Pierre, Fadikar, Arindam, Gramacy, Robert B., Herbei, Radu, Higdon, David, Huang, Jiangeng, Johnson, Leah R., Ma, Pulong, Mondal, Anirban, Pires, Bianica, Sacks, Jerome, Sokolov, Vadim
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models --
Externí odkaz:
http://arxiv.org/abs/2002.01321
Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challeng
Externí odkaz:
http://arxiv.org/abs/1906.07906
Publikováno v:
In Journal of Materials Processing Tech. May 2022 303