22 papers · ranked by Valyu relevance
Joseph Hart, Bart van Bloemen Waanders
Model discrepancy, defined as the difference between model predictions and reality, is ubiquitous in computational models for physical systems. It is common to derive partial differential equations (PDEs) from first principles physics, but make simplifying assumptions to produce tractable expressions for the governing…
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…
Joseph Hart, Bart van Bloemen Waanders
A common goal throughout science and engineering is to solve optimization problems constrained by computational models. However, in many cases a high-fidelity numerical emulation of systems cannot be optimized due to code complexity and computational costs which prohibit the use of intrusive and many query algorithms.…
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…
Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz
Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of…
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…
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…
Michael Y. Li, Vivek Vajipey, Noah D. Goodman, Emily B. Fox
Understanding the world through models is a fundamental goal of scientific research. While large language model (LLM) based approaches show promise in automating scientific discovery, they often overlook the importance of criticizing scientific models. Criticizing models deepens scientific understanding and drives the…
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…
Aaron L. Brown, Lei Shi, Matteo Salvador, Fanwei Kong + 4 more
We present a computational framework for constructing patient-specific models of cardiac mechanics based on standard clinical data, including electrocardiogram (ECG), cuff blood pressure, and electrocardiography-gated computed tomography angiography (CTA) imaging. The model is coupled to a closed-loop lumped parameter…
San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima
Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control Authors: San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima This paper introduces a novel framework for implementing dynamic discrepancy reduced-order modeling in advanced process control. This framework balances…
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…
Chen Cheng, Beiying Liu, Jinxin Cheng, Xiao Xiong + 1 more
Accurate prediction of Electro-Discharge Machining (EDM) results is crucial for industrial applications, aiming to achieve high-performance and cost-efficient machining. However, both the current physical model and the standard Artificial Neural Network (ANN) model exhibit inherent limitations, failing to fully meet…
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…
Mohsen Zaker Esteghamati, Brennan Bean, Henry Burton, M.Z. Naser
structural engineering applications Authors: ['Mohsen Zaker Esteghamati' 'Brennan Bean' 'Henry Burton' 'M.Z. Naser'] Machine learning (ML)-based solutions are rapidly changing the landscape of many fields, including structural engineering. Despite their promising performance, these approaches are usually only…
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)…