title: Multirate method for co-simulation of electrical-chemical systems in multiscale modeling.
abstract: Multiscale modeling by means of co-simulation is a powerful tool to address many vital questions in neuroscience. It can for example be applied in the study of the process of learning and memory formation in the brain. At the same time the co-simulation technique makes it possible to take advantage of interoperability between existing tools and multi-physics models as well as distributed computing. However, the theoretical basis for multiscale modeling is not sufficiently understood. There is, for example, a need of efficient and accurate numerical methods for time integration. When time constants of model components are different by several orders of magnitude, individual dynamics and mathematical definitions of each component all together impose stability, accuracy and efficiency challenges for the time integrator. Following our numerical investigations in Brocke et al. (Frontiers in Computational Neuroscience, 10, 97, 2016), we present a new multirate algorithm that allows us to handle each component of a large system with a step size appropriate to its time scale. We take care of error estimates in a recursive manner allowing individual components to follow their discretization time course while keeping numerical error within acceptable bounds. The method is developed with an ultimate goal of minimizing the communication between the components. Thus it is especially suitable for co-simulations. Our preliminary results support our confidence that the multirate approach can be used in the class of problems we are interested in. We show that the dynamics ofa communication signal as well as an appropriate choice of the discretization order between system components may have a significant impact on the accuracy of the coupled simulation. Although, the ideas presented in the paper have only been tested on a single model, it is likely that they can be applied to other problems without loss of generality. We believe that this work may significantly contribute to the establishment of a firm theoretical basis and to the development of an efficient computational framework for multiscale modeling and simulations.
authors.affiliation: Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden. ekaterina.brocke@scilifelab.se.|National Centre for Biological Sciences, Bangalore, India. ekaterina.brocke@scilifelab.se.|Manipal University, Manipal, India. ekaterina.brocke@scilifelab.se.
authors.forename: Ekaterina
authors.identifier: http://orcid.org/0000-0001-8678-910X
authors.initials: E
authors.lastname: Brocke
authors.affiliation: PDC Center for High-Performance Computing, KTH Royal Institute of Technology, Stockholm, Sweden.
authors.forename: Mikael
authors.initials: M
authors.lastname: Djurfeldt
authors.affiliation: National Centre for Biological Sciences, Bangalore, India.
authors.forename: Upinder S
authors.initials: US
authors.lastname: Bhalla
authors.affiliation: Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.|Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
authors.forename: Jeanette Hellgren
authors.initials: JH
authors.lastname: Kotaleski
authors.affiliation: Department of Mathematics, School of Engineering Sciences, KTH Royal Institute of Technology, Stockholm, Sweden.
authors.forename: Michael
authors.initials: M
authors.lastname: Hanke
mesh_terms: D000465:Algorithms; D004560:Electricity; D004563:Electrochemistry; D006801:Humans; D008959:Models, Neurological; D009488:Neurosciences
keywords: Adaptive time step integration; Backward differentiation formula; Co-simulation; Coupled integration; Coupled system; Multirate integration; Multiscale modeling; Multiscale simulation; Parallel numerical integration