Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Pagendam, Daniel"'
Traditional approaches for learning on categorical data underexploit the dependencies between columns (\aka fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we pro
Externí odkaz:
http://arxiv.org/abs/2307.09321
Autor:
Dabrowski, Joel Janek, Pagendam, Daniel Edward, Hilton, James, Sanderson, Conrad, MacKinlay, Daniel, Huston, Carolyn, Bolt, Andrew, Kuhnert, Petra
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a le
Externí odkaz:
http://arxiv.org/abs/2212.00970
We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks. The approach is more scalable to large data than Markov Chain Monte Carlo, it embraces more expressive models than Variational I
Externí odkaz:
http://arxiv.org/abs/2209.02188
Autor:
Davoudabadi, Mohammad Javad, Pagendam, Daniel, Drovandi, Christopher, Baldock, Jeff, White, Gentry
Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in terrestrial ecosystems. Some studies in the literature ignore the effect
Externí odkaz:
http://arxiv.org/abs/2201.01564
Innovative Approaches in Soil Carbon Sequestration Modelling for Better Prediction with Limited Data
Autor:
Davoudabadi, Mohammad Javad, Pagendam, Daniel, Drovandi, Christopher, Baldock, Jeff, White, Gentry
Publikováno v:
Sci Rep 14, 3191 (2024)
Soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the Paris and Kyoto protocol agreements. Land managers typically rely on computationally complex mod
Externí odkaz:
http://arxiv.org/abs/2105.04789
Publikováno v:
Computers and Electronics in Agriculture, Volume 168, 2020, 105120, ISSN 0168-1699
The contribution of this study is a novel approach to introduce mean reversion in multi-step-ahead forecasts of state-space models. This approach is demonstrated in a prawn pond water quality forecasting application. The mean reversion constrains for
Externí odkaz:
http://arxiv.org/abs/2002.11228
Autor:
Davoudabadi, Mohammad Javad, Pagendam, Daniel, Drovandi, Christopher, Baldock, Jeff, White, Gentry
Publikováno v:
In Environmental Modelling and Software October 2023 168
Autor:
Davoudabadi, Mohammad Javad1,2,3,4 (AUTHOR) mohammadjavad.davoudabadi@hdr.qut.edu.au, Pagendam, Daniel4 (AUTHOR), Drovandi, Christopher1,2,3 (AUTHOR), Baldock, Jeff5 (AUTHOR), White, Gentry1,2,3 (AUTHOR)
Publikováno v:
Scientific Reports. 2/7/2024, Vol. 14 Issue 1, p1-13. 13p.
Autor:
Dabrowski, Joel Janek, Pagendam, Daniel Edward, Hilton, James, Sanderson, Conrad, MacKinlay, Daniel, Huston, Carolyn, Bolt, Andrew, Kuhnert, Petra
Publikováno v:
In Spatial Statistics June 2023 55
The aim of the history matching method is to locate non-implausible regions of the parameter space of complex deterministic or stochastic models by matching model outputs with data. It does this via a series of waves where at each wave an emulator is
Externí odkaz:
http://arxiv.org/abs/1710.03133