20 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…
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…
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…
Matteo Pozzi, Jacopo Marconi, Shobhit Jain, Mingwu Li + 1 more
'Francesco Braghin'] This work presents an optimization framework for tailoring the nonlinear dynamic response of lightly damped mechanical systems using Spectral Submanifold (SSM) reduction. We derive the SSM-based backbone curve and its sensitivity with respect to parameters up to arbitrary polynomial orders…
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…
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…
Lars Thorben Neustock, Paul C. Hansen, Zachary E. Russell, Lambertus Hesselink
'Lambertus Hesselink'] We present a computer-aided design tool for ion optical devices using the adjoint variable method. Numerical methods have been essential for the development of ion optical devices such as electron microscopes and mass spectrometers. Yet, the detailed computational analysis and optimization of ion…
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…
Sebastien Corner, Corina Sandu, Adrian Sandu
Sensitivity analysis of multibody systems computes the derivatives of general cost functions that depend on the system solution with respect to parameters or initial conditions. This work develops adjoint sensitivity analysis for hybrid multibody dynamic systems. Hybrid systems are characterized by trajectories that…
Polina Lakrisenko, Dilan Pathirana, Daniel Weindl, Jan Hasenauer + 1 more
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires a large number of model simulations and objective function gradient computations, if gradient-based optimization is used. In many cases, steady-state computation is a part of model…
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…
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…
Olivia Eriksson, Andrei Kramer, Federica Milinanni, Pierre Nyquist
In this paper we develop a new method for numerically approximating sensitivities in parameter-dependent ordinary differential equations (ODEs). Our approach, intended for situations where the standard forward and adjoint sensitivity analysis become too computationally costly for practical purposes, is based on the…
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)…
Pedro Gonnet, Sotiris Dimopoulos, Lukas Widmer, Jörg Stelling
Background Dynamic mathematical models in the form of systems of ordinary differential equations (ODEs) play an important role in systems biology. For any sufficiently complex model, the speed and accuracy of solving the ODEs by numerical integration is critical. This applies especially to systems identification…
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…