The University of Manchester and Heidelberg University are co-organising a workshop on Surrogates and Dimension Reduction in Machine Learning.
Scientific machine learning has increasingly focused on surrogates for solving partial differential equations, modelling dynamical systems, and learning physical phenomena from data. These surrogates offer efficient and scalable alternatives to traditional methods, with significant potential across diverse applications. However, challenges persist in ensuring stability and providing robust theoretical guarantees. This workshop will explore recent advances in surrogate modelling, emphasising dimension reduction, effective training strategies, and methods that enhance accuracy and theoretical rigour.
Plenary Speakers
Olga Fink, EPFL, Switzerland
Olga Mula, TU Eindhoven, Netherlands
George Stepaniants, California Institute of Technology, USA
Ricardo Vinuesa (online), University of Michigan, US
Talks
Eviatar Bach (online). University of Reading, UK
Nicola Rares Franco. MOX, Politecnico di Milano, Italy
Fernando Henríquez, TU Wien, Austria
Benno Huber, Heidelberg University, Germany
Dibyakanti Kumar, The University of Manchester, UK
Sivalingam S M (online), NIT Puducherry, India
Romit Maulik (online), Pennsylvania State University, US & Argonne National Laboratory, US
Bogdan Raoni? (online), ETH Zürich, Switzerland
Niklas Reinhardt, Heidelberg University, Germany
Thomas O'Leary-Roseberry, UT Austin, US
Anshima Singh, The University of Manchester, UK
Full schedule: https://drsciml.github.io/drsciml/
Organisers
Anirbit Mukherjee, The University of Manchester
Jakob Zech, Heidelberg University
Mauricio A Álvarez, The University of Manchester
Tobias Buck, Heidelberg University
Student Volunteer
Dibyakanti Kumar, The University of Manchester
