22 papers · ranked by Valyu relevance
Sumeet Trehan, Kevin Carlberg, Louis J. Durlofsky
A machine-learning-based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed 'error indicators' (i.e., features)…
Sonya M. Hanson, Sean Ekins, John D. Chodera
All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate…
L. Mark Berliner, Radu Herbei, Christopher K. Wikle, Ralph F. Milliff + 1 more
'Ralph F. Milliff' 'Pablo Martin Rodriguez'] Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We…
Robert Chew, Stephanie Eckman, Christoph Kern, Frauke Kreuter
Supervised machine learning assumes that labeled data provide accurate measurements of the concepts models are meant to learn. Yet in practice, human labeling introduces systematic variation arising from ambiguous items, divergent interpretations, and simple mistakes. Machine learning research commonly treats all…
Benjamin Engelhardt, Holger Frőhlich, Maik Kschischo
Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open.…
Heinrich Peters, Alireza Hashemi, James DeShaw Rae
Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This paper presents a predictive error model trained to detect…
Scott M. Robeson, Cort J. Willmott, Fabiana Zama
When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean…
Moritz Feigl, Benjamin Roesky, Mathew Herrnegger, Karsten Schulz + 1 more
'Masaki Hayashi'] Title: Abstract Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur.…
Yusik Kim
Rule-based classification models described in the language of logic directly predict boolean values, rather than modeling a probability and translating it into a prediction as done in statistical models. The vast majority of existing uncertainty quantification approaches rely on models providing continuous output not…
Azam Moosavi, Vishwas Rao, Adrian Sandu
Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the…
Sebastian Kersting, Michael Köhler
Uncertainty quantification of complex technical systems is often based on a computer model of the system. As all models such a computer model is always wrong in the sense that it does not describe the reality perfectly. The purpose of this article is to give a review of techniques which use observed values of the…
Stephanie L. Bailey, Rose S. Bono, Denis Nash, April D. Kimmel + 1 more
We classified and characterized unintentional errors identified in Eqs (11-3)$1$-(11-4)$3$. Unintentional errors were classified as due to: incorrect cell names, incorrect cell references, incorrect range(s) in a formula, incorrectly copied formulae, overwritten formulae, and misuse of built-in functions . For each…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Woosub Shin, Joseph L. Hellerstein
The growing complexity of reaction-based models necessitates early detection and resolution of model errors. This paper addresses mass balance errors, discrepancies between the mass of reactants and products in reaction specifications. One approach to detection is atomic mass analysis, which uses meta-data to expose…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Authors not listed
Material properties calculated using density functional theory (DFT) are often corrected to more closely match experimental values, but the most common correction method has flaws that lead to unphysical results and false positives in the material discovery process. In this work, we show that these flaws stem from the…
James Nevin, Michael Lees, Paul Groth
Title: Summary Many computational models rely on real-world data, and the steps required in moving from data collection, to data preparation, to model calibration, and input are becoming increasingly complex. Errors in data can lead to errors in model output that might invalidate conclusions in extreme cases. While the…
Yeesock Kim, Hee June Choi, Kwonmoo Lee
In this paper, a new biological modeling approach is proposed for predicting complex heterogeneous subcellular behaviors. Cell protrusion which initiates cell migration has a significant amount of subcellular heterogeneity in micrometer length and minute time scales. It is driven by actin polymerization, e.g., pushing…
Emma Sofie Skarstein, Leonardo Soares Bastos, Håvard Rue, Stefanie Muff
nested Laplace approximations Authors: ['Emma Sofie Skarstein' 'Leonardo Soares Bastos' 'Håvard Rue' 'Stefanie Muff'] Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to adjust for…
Surajit Nandi, Jonas Busk, Peter Bjørn Jørgensen, Tejs Vegge + 1 more
Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this…
Matthew Witman, Peter Schindler
Machine learning (ML) models in the materials sciences that are validated by overly simplistic cross-validation (CV) protocols can yield biased performance estimates for downstream modeling or materials screening tasks. This can be particularly counterproductive for applications where the time and cost of failed…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…