Machine-learning-nederland December 2023

machine-learning-nederland@list.uva.nl

3 participants
3 discussions

Call for abstracts for “Fundamental Sciences in AI” at ICT.OPEN2024
by Dirksen, S. (Sjoerd) 15 Jan '24

15 Jan '24

Dear all, Each year, the AI and Mathematics network (AIM<https://aimath.nl/>) organizes a track at the conference ICT.OPEN<https://ictopen.nl/>. The next edition will take place on April 10 and 11, 2024, in the Jaarbeurs in Utrecht. The goal of the track is to bring together mathematicians and computer scientists who are based in the Netherlands and are working on the foundations of AI. A detailed track description can be found here<https://ictopen.nl/track-fundamental-sciences-in-ai>. We would like to invite you to submit an abstract to present your work within the track. The details of the call can be found here<https://ictopen.nl/call-for-abstracts-nwo-ictopen2024>. The submission deadline is 16 January 2024. Best wishes, Sjoerd Dirksen, Tim van Erven, Silke Glas, Mihaela Mitici

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Vacancy: Assistant Professors in Mathematical Statistics and Stochastic Processes - Vrije Universiteit Amsterdam
by Heide, R. de (Rianne) 22 Dec '23

22 Dec '23

Dear colleagues, the Department of the Mathematics of the Vrije Universiteit Amsterdam is hiring 2 new Assistant Professors in Mathematical Statistics and Stochastic Processes. We are particularly interested in candidates that work at the intersection between theory and applications. This may include, but is not restricted to, mathematical statistics, biostatistics, statistics for life sciences, causal inference, mathematics of machine learning, data assimilation and applications in neuroscience, biology, health care, movement sciences and economics. For more information and to apply, please see https://vu.nl/en/about-vu/more-about/joining-us-mathematics The deadline for applications is the 4th of February. Please disseminate this advert in your network and forward to any potential candidate. Best wishes, Rianne de Heide Assistant Professor Department of Mathematics Vrije Universiteit Amsterdam https://riannedeheide.github.io

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CWi SciML semester ++ talk Giovanni Stabile
by Wouter Edeling 05 Dec '23

05 Dec '23

Dear all, Monday December 11th at 11h00CET (in the L120 room) the 4th and final seminar++ talk from the CWI semester programme on Scientific Machine Learning will take place. Giovanni Stabile from the University of Urbino in Italy will present new work on nonlinear model order reduction, the full abstract can be found below. Online attendence is possible via: Join Zoom Meeting https://cwi-nl.zoom.us/j/82378289134?pwd=djNCTHI4SjE2K1NhWFVhQW5yTzRxZz09 Meeting ID: 823 7828 9134 Passcode: 139939 Feel free to share the link with others. Kind regards, Wouter Edeling ------------------ Title: From linear to nonlinear model order reduction: some results and perspectives Abstract: Non-affine parametric dependencies, nonlinearities, and advection-dominated regimes of the model of interest can result in a slow Kolmogorov n-width decay, which precludes the realization of efficient reduced-order models based on Proper Orthogonal Decomposition. Among the possible solutions, there are purely data-driven methods that leverage nonlinear approximation techniques such as autoencoders and their variants to learn a latent representation of the dynamical system, and then evolve it in time with another architecture. Despite their success in many applications where standard linear techniques fail, more has to be done to increase the interpretability of the results, especially outside the training range and not in regimes characterized by an abundance of data. Not to mention that none of the knowledge on the physics of the model is exploited during the predictive phase. In this talk, in order to overcome these weaknesses, I introduce a variant of the nonlinear manifold method introduced in previous works with hyper-reduction achieved through reduced over-collocation and teacher-student training of a reduced decoder. We test the methodology on different problems with increasing complexity.

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Machine-learning-nederland December 2023