20 papers · ranked by Valyu relevance
Chon Lok Lei, Sanmitra Ghosh, David J. Whittaker, Yasser Aboelkassem + 13 more
'Kylie A. Beattie' 'Chris D. Cantwell' 'Tammo Delhaas' 'C. Stuart Houston' 'Gustavo Montes Novaes' 'Alexander V. Panfilov' 'Pras Pathmanathan' 'Marina Riabiz' 'Rodrigo Weber dos Santos' 'John Walmsley' 'Keith Worden' 'Gary R. Mirams' 'R. Wilkinson'] Chon Lok Lei1 , Sanmitra Ghosh2 , Dominic G. Whittaker3 , Yasser…
Gevik Grigorian, Victoria Volodina, Samiran Ray, Francisco Alejandro DiazDelao + 1 more
'Francisco Alejandro DiazDelao' 'Claire Black'] Many mathematical models suffer from model discrepancy, posing a significant challenge to their use in clinical decision-making. In this article, we consider methods for addressing this issue. In the first approach, a mathematical model is treated as a black box system…
Michael Goldstein, Ian Vernon, Jonathan A. Cumming
Model or structural discrepancy is an essential component in the analysis of computer simulators, representing the differences between the outputs of the simulator and the real-world system that the simulator seeks to represent. This discrepancy can arise from various sources such as simplifications of the model…
Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker, Yasser Aboelkassem + 13 more
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in…
Wenyu Li, Arun Hegde, James Oreluk, Andrew Packard + 1 more
'Michael Frenklach'] We extended the existing methodology in Bound-to-Bound Data Collaboration (B2BDC), an optimization-based deterministic uncertainty quantification (UQ) framework, to explicitly take into account model discrepancy. The discrepancy was represented as a linear combination of finite basis functions and…
Joseph G. Shuttleworth, Chon Lok Lei, Dominic G. Whittaker, Monique J. Windley + 3 more
'Monique J. Windley' 'Adam P. Hill' 'Simon P. Preston' 'Gary R. Mirams'] When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological…
Rebecca E. Morrison, Américo Cunha
Mathematical models of epidemiological systems enable investigation of and predictions about potential disease outbreaks. However, commonly used models are often highly simplified representations of incredibly complex systems. Because of these simplifications, the model output, of say new cases of a disease over time…
Todd Oliver, Gabriel Terejanu, Christopher S. Simmons, Robert Moser
The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction…
Pierre Barbillon, Anabel Forte, Rui Paulo
Screening traditionally refers to the problem of detecting active inputs in the computer model. In this paper, we develop methodology that applies to screening, but the main focus is on detecting active inputs not in the computer model itself but rather on the discrepancy function that is introduced to account for…
Megan R. Ebers, Michael C. Rosenberg, J. Nathan Kutz, Katherine M. Steele
We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual’s response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural…
Matthew J. Simpson, Ruth E. Baker, Pascal R. Buenzli, Ruanui Nicholson + 1 more
Stochastic individual-based mathematical models are attractive for modelling biological phenomena because they naturally capture the stochasticity and variability that is often evident in biological data. Such models also allow us to track the motion of individuals within the population of interest. Unfortunately…
Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti
Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement…
Robin Umbra, Ulrike Fasbender
This manuscript introduces the Interaction Discrepancy Model (IDM), a theoretical framework designed to enhance our understanding of person-environment interactions. Traditional models often overlook the dynamic, iterative, and feedback-driven nature of these interactions, typically focusing on episodic and isolated…
Noah Wightman, Isaac Eckert, Brian Leung, Laura J. Pollock
Anticipating biodiversity change is critical in rapidly warming regions, yet challenging because these areas often coincide with poor sampling. Data gaps are widely understood to interfere with species distribution models (SDMs), but this is difficult to detect with biased data. We test SDM bias-correction methods with…
Authors not listed
In molecular machine learning, the choice of the representation of molecules can have a significant impact on model performance. However, understanding the root causes of these performance differences often proves challenging. One promising approach to explore model behavior is representational alignment, which…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Authors not listed
The precision of thermodynamic modeling for ionic liquid (IL)–solute systems is fundamentally reliant on the quality of experimental data. However, prevalent databases such as ILThermo frequently exhibit conflicting measurements for the same systems under identical temperature and pressure conditions. These disparities…
Devi Ganapathi, Wunmi Akinlemibola, Antonio Baclig, Emily Penn + 1 more
Quinones and hydroquinones are small organic molecules with numerous applications: battery electrolytes, pharmaceuticals, sensors, to name a few. An understanding of their fundamental properties, such as melting points, is essential to incorporate these compounds into relevant technologies. In this study, two different…
Authors not listed
Range anxiety remains a major concern for electric vehicle (EV) drivers due to unpredictable charge usage influenced by terrain and user behavior variations. To address this issue, we propose a data-driven approach to provide accurate trip-specific battery consumption for EV drivers. First, we present a new…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…