29 papers · ranked by Valyu relevance
Jinling Chi, Chang Wang, Yangxue He, Chenxu Gou + 2 more
In gas turbine simulation, precise parameterization of components is essential for reliable performance prediction, yet manufacturers usually provide only limited operational data. To address this issue, this study proposes a modeling approach based on limited operational parameters and applies it to a 9FA heavy-duty…
Stephen B. Broomell, Sabina J. Sloman, Lisheng He
Behavioral models are instrumental for studying human cognition, yet many inferences derived from such models fail to generalize. We argue that this is driven in part by the increasing complexity of behavioral models, where non-linearities and discontinuities create dynamic parameter interactions that limit the…
Dasuni Amanda Salpadoru, Matthew P. Adams, Kate Helmstedt, David J. Warne
Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is…
Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak‐Czochra, Zuzanna Szymańska
Calibrating mathematical models of biological processes is essential for achieving predictive accuracy and gaining mechanistic insight. However, this task remains challenging due to limited and noisy data, significant biological variability, and the computational complexity of the models themselves. In this method's…
Aleksandr Talitckii, Matthew Peet
The attitude dynamics of aircraft and spacecraft exhibit significantly nonlinear behaviour. In spacecraft, torque is generated through reaction wheels and control moment gyros. In aircraft, torque is generated using lift on control surfaces. In both cases, complex geometries, unique configurations, and…
Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak-Czochra, Zuzanna Szymańska
Calibrating mathematical models of biological processes is essential for achieving predictive accuracy and gaining mechanistic insight. However, this task remains challenging due to limited and noisy data, significant biological variability, and the computational complexity of the models themselves. In this method’s…
Brock D. Sherlock, Marko A. A. Boon, Maria Vlasiou, Adelle C. F. Coster
Measurement error is an unavoidable feature of experimental data collection. It is common in mathematical biology to consider measurement error in the dependent variable. However, less attention has been given to errors in the independent variable. This work is focussed on the effects of independent variable…
Amal Chebbi, Matthew A. Franchek, Karolos Grigoriadis
Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review…
Sascha Ranftl, Malte Rolf, Gerhard A. Holzapfel, Ellen Kuhl
Uncertainty quantification (UQ) is essential to experimental mechanics, but has become particularly relevant in computational mechanics, manifesting in two fundamental problem types: forward and inverse problems. The former addresses how input uncertainties propagate to the quantities of interest, whereas the latter…
Authors not listed
Knowledge of the reaction rate constants can be vital in understanding electrochemical reaction mechanisms and their rate-determining processes. Although first-principles methods, such as density functional theory (DFT), provide valuable insight into reaction free energies and rate constants, they commonly use…
Peter Graham Clark, Lea Emmy Timpen, Alexander Heberle, Martina Prugger + 4 more
Parameterisation of dynamic biochemical network models is a challenging aspect of systems biology. Especially when the parameter space is large and data is semi quantitive but comparable across different experimental conditions. Here, we present a set of command line tools utilising Pycotools (COPASI) that leverages…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Piotr Bania, Anna Wójcik, Ercan Kuruoglu
The design of informatively rich input signals is essential for accurate system identification, yet classical Fisher-information-based methods are inherently local and often inadequate in the presence of significant model uncertainty and non-linearity. This paper develops a Bayesian approach that uses the mutual…
Authors not listed
Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
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…
Adarshkrishnan Rajakumar, Pascal R. Buenzli, Matthew J. Simpson
Understanding and predicting extinction risk is a central challenge in population biology. Mathematical models incorporating Allee thresholds are commonly used to understand population dynamics and to assess extinction risks. Inaccurate predictions can have serious consequences for conservation management. In this…
Genshiro Kitagawa
The non-Gaussian filter provides a deterministic numerical method for nonlinear and non-Gaussian state-space models, but its application has long been limited due to the computational cost of numerical integration. Advances in computing power and memory capacity have substantially reduced this limitation for low and…
Conor Rowan
The explicit constraint force method (ECFM) was recently introduced as a novel formulation of the physics-informed solution reconstruction problem, and was subsequently extended to inverse problems. In both solution reconstruction and inverse problems, model parameters are estimated with the help of measurement data.…
Josef Dick, Quôc Thông Lê Gia, Kassem Mustapha
This work uses the Bayesian inference technique to infer the Young modulus from the stochastic linear elasticity equation. The Young modulus is modeled by a finite Karhunen Lo´eve expansion, while the solution to the linear elasticity equation is approximated by the finite element method. The high dimensional integral…
Haidy A. Newer
We propose a statistical model for multicomponent stress-strength reliability under the inverted exponentiated Rayleigh distribution. The model is specifically designed for complex data structures where component strength is measured using block adaptive Type-II progressive hybrid censoring, while operational stress is…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
Rabea Turon, Lars C. Reining, Philipp A. Hummel, Lynn Schmittwilken + 5 more
Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants’ responses. However, in high-dimensional spaces, identifying…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Xiaolu Wang, Peter Dayan, Paul M Bays
The activity of neural populations typically encodes more information about sensory or motor variables than can be captured by point estimates of the variables. We present and compare two approaches to quantifying this additional or ancillary information and its relationship to uncertainty: the mutual information…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Maria Selezneva, Konstantin Neusypin, Anastasia Surkova
This study investigates the concept of the adaptability degree of state variables in mathematical models used in estimation algorithms. Two numerical criteria are proposed for calculating the adaptability degree of state variables in linear models. Qualitative characteristics of the adaptability of inertial navigation…
Nils Lid Hjort
This document is a pdf generated from old plain-TeX files of 1986, of Nils Lid Hjort's `Notes on the Theory of Statistical Symbol Recogntion', a limited circulation 207-pages monograph published at the Norwegian Computing Centre, as Report no. 778/1986. It gives the basics of the statistical pattern recognition theory…
E. G. Cooch, D. I. MacKenzie, J. A. Royle
Data augmentation is now a standard device across capture–recapture and occupancy analysis: adding a fixed number M of all-zero encounter histories replaces a model of unknown dimension with one of fixed dimension. Although M is often treated as a computational tuning choice, it also specifies a finite superpopulation…
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…