19 papers · ranked by Valyu relevance
Brian Tran, Melvin Leok
Adjoint systems are widely used to inform control, optimization, and design in systems described by ordinary differential equations or differential-algebraic equations. In this paper, we explore the geometric properties and develop methods for such adjoint systems. In particular, we utilize symplectic and presymplectic…
Harry Dankowicz, Jan Sieber
This paper presents a rigorous framework for the continuation of solutions to nonlinear constraints and the simultaneous analysis of the sensitivities of test functions to constraint violations at each solution point using an adjoint-based approach. By the linearity of a problem Lagrangian in the associated Lagrange…
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…
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…
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 + 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…
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…
Defne Ege Ozan, Luca Magri
Data-Driven Approach Authors: ['Defne Ege Ozan' 'Luca Magri'] Abstract. In one calculation, adjoint sensitivity analysis provides the gradient of a quantity of interest with respect to all system's parameters. Conventionally, adjoint solvers need to be implemented by differentiating computational models, which can be a…
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…
John Guillamon, Cheng-Zhen Wang, Zin Lin, Tsampikos Kottos
Controlling electromagnetic wave propagation in multiple scattering systems is a challenging endeavor due to the extraordinary sensitivity generated by strong multi-path contributions at any given location. Overcoming such complexity has emerged as a central research theme in recent years, motivated both by a wide…
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)…
Élise Grosjean, Bernd Simeon
This paper deals with the derivation of Non-Intrusive Reduced Basis (NIRB) techniques for sensitivity analysis, more specifically the direct and adjoint state methods. For highly complex parametric problems, these two approaches may become too costly ans thus Reduced Basis Methods (RBMs) may be a viable option. We…
Pranshul Thakur, Siva Nadarajah
Domain Discretization Authors: ['Pranshul Thakur' 'Siva Nadarajah'] Abstract. Chaotic dynamical systems are characterized by the sensitive dependence of trajectories on initial conditions. Conventional sensitivity analysis of time-averaged functionals yields unbounded sensitivities when the simulation is chaotic. The…
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…
Gabriel Peery, Toni M. West, Sanjana S. Chemuturi, Jodie H. Pham + 2 more
We have recently documented significant compressible behaviors in hydrogels implemented in 3D traction force microscopy (TFM). Therefore, here we have developed a new computational pipeline that accounts for this observation. Additionally, the new method accurately recovers large ranges and spatial heterogeneity of…
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…
Fauzia Jabeen, Silvana Ilie
Biochemical reaction systems in a cell exhibit a stochastic behaviour, owing to the unpredictable nature of the molecular interactions. The fluctuations at the molecular level may lead to a different behaviour than that predicted by the deterministic model of the reaction rate equations, when some reacting species have…
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…
Peter J. Gawthrop, Michael Pan
The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing…