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21 papers · ranked by Valyu relevance
Fabian Fröhlich, Barbara Kaltenbacher, Fabian J. Theis, Jan Hasenauer + 1 more
'Jan Hasenauer' 'Jorg Stelling'] Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the…
Amanda Joy Pappin, Amir Hakami
Background: Decision making regarding air pollution can be better informed if air quality impacts are traced back to individual emission sources. Adjoint or backward sensitivity analysis is a modeling tool that can achieve this goal by allowing for quantification of how emissions from sources in different locations…
Polina Lakrisenko, Paul Stapor, Stephan Grein, Łukasz Paszkowski + 6 more
'Dilan Pathirana' 'Fabian Fröhlich' 'Glenn Terje Lines' 'Daniel Weindl' 'Jan Hasenauer' 'Attila Csikász-Nagy'] Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from…
Paul Stapor, Fabian Fröehlich, Jan Hasenauer
Parameter estimation methods for ordinary differential equation (ODE) models of biological processes can exploit gradients and Hessians of objective functions to achieve convergence and computational efficiency. However, the computational complexity of established methods to evaluate the Hessian scales linearly with…
Fabian Fröhlich, Barbara Kaltenbacher, Fabian J. Theis, Jan Hasenauer
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small-and medium-scale biological processes. While the same should in principle hold for large-and genome-scale processes, the computational methods for the analysis of…
Radu Serban, Antonio M. Recuero
We present an adjoint sensitivity method for hybrid discrete – continuous systems, extending previously published forward sensitivity methods. We treat ordinary differential equations and differential-algebraic equations of index up to two (Hessenberg) and provide sufficient solvability conditions for consistent…
Mohamed Hayek, Jeremy T. White, Katherine H. Markovich, Joseph D. Hughes + 1 more
Adjoint sensitivity analysis provides an efficient alternative to direct methods when evaluating the influence of many uncertain parameters on a limited number of performance measures in hydrologic and hydrogeologic models. However, most adjoint implementations are “intrusive”, requiring extensive modifications of the…
Kelli Humbird, Ryan G. McClarren
Uncertainty quantification and sensitivity analyses are a vital component for predictive modeling in the sciences and engineering. The adjoint approach to sensitivity analysis requires solving a primary system of equations and a mathematically related set of adjoint equations. The information contained in the equations…
Polina Lakrisenko, Paul Stapor, Stephan Grein, Łukasz Paszkowski + 5 more
Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However…
Guojun Hu, Tomasz Kozłowski
Verification, validation and uncertainty quantification (VVUQ) have become a common practice in thermal-hydraulics analysis. An important step in the uncertainty analysis is the sensitivity analysis of various uncertainty input parameters. The efficient method for computing the sensitivities is the adjoint method. The…
Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer + 1 more
Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for…
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange + 1 more
Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to…
Guojun Hu, Tomasz Kozłowski
Verification, validation and uncertainty quantification (VVUQ) have become a common practice in thermal-hydraulics analysis. An important step in the uncertainty analysis is the sensitivity analysis of various uncertain input parameters. The common approach for computing the sensitivities, e.g. variance-based and…
Rachel Mester, Alfonso Landeros, Chris Rackauckas, Kenneth Lange
Differential sensitivity analysis is indispensable in fitting parameters, understanding uncertainty, and forecasting the results of both thought and lab experiments. Although there are many methods currently available for performing differential sensitivity analysis of biological models, it can be difficult to…
Rui Escadas Martins, Evgeny Lakshtanov
for explicit adaptive Runge-Kutta methods enabled by automatic adjoint differentiation and SIMD vectorization Authors: ['Rui Escadas Martins' 'Evgeny Lakshtanov'] A C++ library for sensitivity analysis of optimisation problems involving ordinary differential equations (ODEs) enabled by automatic differentiation (AD)…
Yitao Zhu, Daniel Dopico, Corina Sandu, Adrian Sandu
Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure…
Chao Xu, Emily Mazeau, Richard West
Mean-field micro-kinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a micro-kinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and…
Authors not listed
We comment on the work on convex regions of the potential energy surface (PES) of a molecule by M. Gunde; A. Jay; M. Poberˇznik; N. Salles; N. Richard; G. Landa; N. Mousseau; L. Martin-Samos and A. Hemeryck [J. Chem. Phys. 160, 232501 (2024)]. In contrast to the activation-relaxation technique nouveau (ARTn), in the…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
Authors not listed
Amines play a significant role in everyday life, and their detection remains a crucial focus in research and development. Although conducting polymer-based gas sensors have been widely reported for amine detection using DC resistivity measurements, they often lack selectivity to distinguish between intragroup…
Itamar Borges Jr, Júlio César Duarte, Romulo Dias da Rocha
We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, ∑▒〖Q_0 (NO_2)〗 (the charge of the nitro groups)…