arXiv subjects

James Hilton

Publications and source records attributed to James Hilton.

4 recordsLinked to original sources

arXiv2022

Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires

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 level-set function. The result is a PINN that simulates a fire-front as it propagates through the spatio-temporal domain. We show that popular optimisation cost functions used in the literature can result in PINNs that fail to maintain temporal continuity in modelled fire-fronts when there are extreme changes in exogenous forcing variables such as wind direction. We thus propose novel additions to the optimisation cost function that improves temporal continuity under these extreme changes. Furthermore, we develop an approach to perform data assimilation within the PINN such that the PINN predictions are drawn towards observations of the fire-front. Finally, we incorporate our novel approaches into a Bayesian PINN (B-PINN) to provide uncertainty quantification in the fire-front predictions. This is significant as the standard solver, the level-set method, does not naturally offer the capability for data assimilation and uncertainty quantification. Our results show that, with our novel approaches, the B-PINN can produce accurate predictions with high quality uncertainty quantification on real-world data.

cs.LG↗

arXiv2022

Towards Data Assimilation in Level-Set Wildfire Models Using Bayesian Filtering

The level-set method is a prominent approach to modelling the evolution of a fire over time based on a characterised rate of spread. It however does not provide a direct means for assimilating new data and quantifying uncertainty. Fire front predictions can be more accurate and agile if the models are able to assimilate data in real time. Furthermore, uncertainty estimation of the location and spread of the fire is critical for decision making. Using Bayesian filtering approaches, we extend the level-set method to allow for data assimilation and uncertainty quantification. We demonstrate these approaches on data from a controlled fire.

stat.AP↗

arXiv2022

A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models

Computational simulations of wildfire spread typically employ empirical rate-of-spread calculations under various conditions (such as terrain, fuel type, weather). Small perturbations in conditions can often lead to significant changes in fire spread (such as speed and direction), necessitating a computationally expensive large set of simulations to quantify uncertainty. Model emulation seeks alternative representations of physical models using machine learning, aiming to provide more efficient and/or simplified surrogate models. We propose a dedicated spatio-temporal neural network based framework for model emulation, able to capture the complex behaviour of fire spread models. The proposed approach can approximate forecasts at fine spatial and temporal resolutions that are often challenging for neural network based approaches. Furthermore, the proposed approach is robust even with small training sets, due to novel data augmentation methods. Empirical experiments show good agreement between simulated and emulated firefronts, with an average Jaccard score of 0.76.

cs.LG↗

arXiv2016

Nominal values for selected solar and planetary quantities: IAU 2015 Resolution B3

In this brief communication we provide the rationale for, and the outcome of the International Astronomical Union (IAU) resolution vote at the XXIX-th General Assembly in Honolulu, Hawaii, in 2015, on recommended nominal conversion constants for selected solar and planetary properties. The problem addressed by the resolution is a lack of established conversion constants between solar and planetary values and SI units: a missing standard has caused a proliferation of solar values (e.g., solar radius, solar irradiance, solar luminosity, solar effective temperature and solar mass parameter) in the literature, with cited solar values typically based on best estimates at the time of paper writing. As precision of observations increases, a set of consistent values becomes increasingly important. To address this, an IAU Working Group on Nominal Units for Stellar and Planetary Astronomy formed in 2011, uniting experts from the solar, stellar, planetary, exoplanetary and fundamental astronomy, as well as from general standards fields to converge on optimal values for nominal conversion constants. The effort resulted in the IAU 2015 Resolution B3, passed at the IAU General Assembly by a large majority. The resolution recommends the use of nominal solar and planetary values, which are by definition exact and are expressed in SI units. These nominal values should be understood as conversion factors only, not as the true solar/planetary properties or current best estimates. Authors and journal editors are urged to join in using the standard values set forth by this resolution in future work and publications to help minimize further confusion.

astro-ph.SR↗