arXiv subjects

Richard Wood

Publications and source records attributed to Richard Wood.

5 recordsLinked to original sources

arXiv2024

Targeted Calibration to Adjust Stability Biases in Complex Dynamical System Models

Models of complex dynamical systems like the Earth's climate often involve large numbers of uncertain parameters. Comprehensive exploration of the parameter space is typically prohibitive due to excessive computational costs. Systematic gradient-based parameter optimization is not feasible because such models are typically not differentiable. This is especially problematic in cases where the models describe highly nonlinear and possibly abrupt dynamics, where sensitivity to parameter changes is high. Components of Earth's climate system, such as the North Atlantic Overturning Circulation or the polar ice sheets, are at risk of undergoing critical transitions in response to anthropogenic climate change. Concerns have been raised that these Earth system components are too stable in state-of-the-art models. In my presentation, we will see examples how new scenario simulations allow studying the possibility and the consequences of tipping events in Earth system models. Also, we will discuss a method for efficient, systematic, and objective calibration of dynamical complex system models, targeted at adjusting system stability. Given a number of physical or observational constraints, the method can efficiently adjust stability biases in a range of complex system models and help reveal potentially hidden instabilities, with important implications for Earth system modelling.

physics.ao-ph↗

arXiv2024

Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning

Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accurately predicting patient care demand, however, is a ubiquitous challenge for hospitals across the world due, in part, to the demand's time-varying temporal variability, and, in part, to the difficulty in modelling trends in advance. To address this issue, here, we develop two methods, a relatively simple time-vary linear model, and a more advanced neural network model. The former forecasts patient arrivals hourly over a week based on factors such as day of the week and previous 7-day arrival patterns. The latter leverages a long short-term memory (LSTM) model, capturing non-linear relationships between past data and a three-day forecasting window. We evaluate the predictive capabilities of the two proposed approaches compared to two naïve approaches - a reduced-rank vector autoregressive (VAR) model and the TBATS model. Using patient care demand data from Rambam Medical Center in Israel, our results show that both proposed models effectively capture hourly variations of patient demand. Additionally, the linear model is more explainable thanks to its simple architecture, whereas, by accurately modelling weekly seasonal trends, the LSTM model delivers lower prediction errors. Taken together, our explorations suggest the utility of machine learning in predicting time-varying patient care demand; additionally, it is possible to predict patient care demand with good accuracy (around 4 patients) three days or a week in advance using machine learning.

cs.LG↗

arXiv2021

Uniting Parametric Uncertainty and Tipping Diagrams

Various subsystems of the Earth system may undergo critical transitions by passing a so-called tipping point, under sustained changes to forcing. For example, the Atlantic Meridional Overturning Circulation (AMOC) is of particular importance for North Atlantic heat transport and is thought to be potentially at risk of tipping. Given a model of such a subsystem that accurately includes the relevant physical processes, whether tipping occurs or not, will depend on model parameters that typically are uncertain. Reducing this parametric uncertainty is important to understand the likelihood of tipping behavior being present in the system and possible tipping locations. In this letter, we develop improved estimates for the parametric uncertainty by inferring probability distributions for the model parameters based on physical constraints and by using a Bayesian inversion technique. To visualize the impact of parametric uncertainty, we extend classical tipping diagrams by visualizing probabilistic bifurcation curves according to the inferred distribution of the model parameter. Furthermore, we highlight the uncertain locations of tipping points along the probabilistic bifurcation curves. We showcase our probabilistic visualizations of the tipping behavior using a simple box-model of the AMOC, the Stommel-Cessi model [5].

physics.ao-ph↗

arXiv2018

Artificial Retina Using A Hybrid Neural Network With Spatial Transform Capability

This paper covers the design and programming of a hybrid (digital/analog) neural network to function as an artificial retina with the ability to perform a spatial discrete cosine transform. We describe the structure of the circuit, which uses an analog cell that is interlinked using a programmable digital array. The paper is broken into three main parts. First, we present the results of a Matlab simulation. Then we show the circuit simulation in Spice. This is followed by a demonstration of the practical device. This system has intentionally separated components with the specialty analog circuits being separated from the readily available digital field programmable gate array (FPGA) components. Further development includes the use of rapid manufacture-able organic electronics used for the analog components. The planned uses for this platform include crowd development of software that uses the underlying pulse based processing. The development package will include simulators in the form of Matlab and Spice type software platforms.

cs.CV↗

arXiv2004

RF Couplers for Normal-Conducting Photoinjector of High-Power CW FEL

A high-current emittance-compensated RF photoinjector is a key enabling technology for a high-power CW FEL. A preliminary design of a normal-conducting, 2.5-cell pi-mode, 700-MHz CW RF photoinjector that will be built for demonstration purposes, is completed. This photoinjector will be capable of accelerating a 100-mA electron beam (3 nC per bunch at 35 MHz bunch repetition rate) to 2.7 MeV while providing an emittance below 7 mm-mrad at the wiggler. More than 1 MW of RF power will be fed into the photoinjector cavity through two ridge-loaded tapered waveguides. The waveguides are coupled to the cavity by "dog-bone" irises cut in a thick wall. Due to CW operation of the photoinjector, the cooling of the coupler irises is a rather challenging thermal management project. This paper presents results of a detailed electromagnetic modeling of the coupler-cavity system, which has been performed to select the coupler design that minimizes the iris heating due to RF power loss in its walls.

physics.acc-ph↗