University of Birmingham > Talks@bham > Applied Mathematics Seminar Series > Discrete Multiphysics for in-silico modelling of human organs and other biological applications

## Discrete Multiphysics for in-silico modelling of human organs and other biological applicationsAdd to your list(s) Download to your calendar using vCal - Alessio Alexiadis, University of Birmingham
- Thursday 27 February 2020, 12:00-13:00
- Biosciences 301.
If you have a question about this talk, please contact Fabian Spill. The first part of the talk introduces Discrete Multiphysics [1]: a hybrid computational technique based on the combination of discrete, particle-based, models such as Smoothed Particle Hydrodynamics, the Discrete Element Method and the Spring Lattice Model. Its advantages and disadvantages with respect traditional, mesh-based, multiphysics are discussed. The second part applies Discrete Multiphysics to in-silico modelling oh human organs with examples coming from cardiovascular flows [2], deep venous thrombosis [3-4], the lungs [5] and the intestine [6]. Other biological applications (e.g. flows containing cells) are also discussed [8]. The last part of the talk focuses on the combination of Discrete Multiphysics with Machine Learning and in particular Reinforcement Learning. The computational-particles used in Discrete Multiphysics are linked to computational-neurons used in Artificial Intelligence algorithms. This new technique combines the advantage of multiphysics and machine learning and it is particular well-suited to model the effect of the autonomic nervous system in in-silico models of human organs [8]. References [1] Alexiadis A., (2015) The Discrete Multi-Hybrid System for the simulation of solid-liquid flows PLoS ONE 10 (5): e0124678 [2] Ariane M., Allouche H., Bussone M., Giacosa F., Bernard F., Barigou M., Alexiadis A. (2017) Discrete multiphysics: a mesh-free approach to model biological valves including the formation of solid aggregates at the membrane surface and in the flow PloS ONE 12 (4): e0174795 [3] Ariane, M., Vigolo, D., Brill, A., Nash, G. B., Barigou, M., Alexiadis, A. (2018) Using Discrete Multi-Physics for studying the dynamics of emboli in flexible venous valves Computers and Fluids 166:57–63 [4] Ariane M., Wen W., Vigolo D., Brill A., Nash G. B., Barigou M., Alexiadis A. (2017) Modelling and simulation of flow and agglomeration in deep veins valves using Discrete Multi Physics. Computers in Biology and Medicine 89: 96–103 [5] Ariane M., Kassinos S., Velaga S., Alexiadis A. (2018) Discrete multi-physics simulations of diffusive and convective mass transfer in boundary layers containing motile cilia in lungs Computers in Biology and Medicine 95:34–42 [6] Alexiadis A., Stamatopoulos K., Wen W., Bakalis S., Barigou M.,Simmons M. (2017) Using discrete multi-physics for detailed exploration of hydrodynamics in an in vitro colon system Computers in Biology and Medicine 81:188–198 [7] Rahmat A., Barigou M., Alexiadis A. (2019) Deformation and rupture of compound cells under shear: a Discrete Multiphysics Study Physics of Fluids 31:051903 [8] Alexiadis A. (2019) Deep Multiphysics: Coupling Discrete Multiphysics with Machine Learning to attain self-learning in-silico models replicating human physiology Artificial Intelligence in Medicine 98:27-34 This talk is part of the Applied Mathematics Seminar Series series. ## This talk is included in these lists:Note that ex-directory lists are not shown. |
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