The objective of the event is to create an interdisciplinary workshop, which brings together international experts working on mathematical models from various fields. These fields include control theory, material science, medicine, and aerospace, to name a few. Despite the different applications of mathematical models, the underlying concepts related to the modelling of complex processes and systems share many common features. The same holds for the application of scientific computing methods for the numerical implementation of the mathematical models. These methods nowadays constitute an integral part of the research task in applied fields. Topics such as parallel computing on high performance computer systems and the use of shared memory architectures are vital e.g. for the solution of partial differential equations arising in various fields. Rapidly developing technologies like machine learning methods became indispensable in data analysis. It is believed that bringing together experts from different fields will lead to new innovative ideas and create synergies between the scientific research topics.
Our line-up of keynotes and session chairs/speakers is amazing and we encourage you all to submit talks and posters.
Please join us in making ICSB 2022 the best ICSB ever. We are going all in on trying to reboot the community after the pandemic. This meeting will be in person and full of exciting science!
The Call website can be found here on the EasyChair platform:
The main conference website is https://www.icsb2022.berlin
In the completely different discipline of machine learning, a fairly similar phenomenon takes place. The microscopic variables of an image are given by its constituent pixel values. When we analyse the image with a deep neural network (DNN), we will detect edges in the first layer, corners in the next layer, then the object parts and finally, at the top of the neural network, entire objects. We understand the world at the “emergent” level of objects and their relations, not at the level of pixels and edges. In deep learning (DL) emergence happens automatically through learning and some inductive biases such as symmetries.
A major question we want to address in this workshop is whether we can apply the same learning paradigm to the field of molecular science to learn the correct emergent variables and dynamics.
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